{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "介绍如何在tensorflow环境下，使用FGSM算法攻击基于Inception数据集预训练的alexnet模型。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Jupyter notebook中使用Anaconda中的环境需要单独配置，默认情况下使用的是系统默认的Python环境，以使用advbox环境为例。\n",
    "首先在默认系统环境下执行以下命令，安装ipykernel。\n",
    "\n",
    "    conda install ipykernel\n",
    "    conda install -n advbox ipykernel\n",
    "\n",
    "在advbox环境下激活，这样启动后就可以在界面上看到advbox了。\n",
    "\n",
    "    python -m ipykernel install --user --name advbox --display-name advbox \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import logging\n",
    "logging.basicConfig(level=logging.INFO,format=\"%(filename)s[line:%(lineno)d] %(levelname)s %(message)s\")\n",
    "logger=logging.getLogger(__name__)\n",
    "\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "#pip install Pillow\n",
    "from adversarialbox.adversary import Adversary\n",
    "from adversarialbox.attacks.deepfool import DeepFoolAttack\n",
    "from adversarialbox.models.tensorflow import TensorflowModel\n",
    "import tensorflow as tf\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "from tools import show_images_diff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#定义被攻击的图片\n",
    "imagename=\"tutorials/cropped_panda.jpg\"\n",
    "#从'http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz'下载并解压到当前路径\n",
    "dirname=\"classify_image_graph_def.pb\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "tensorflow.py[line:63] INFO self._input_shape:(Dimension(None), Dimension(None), Dimension(3))\n",
      "tensorflow.py[line:65] INFO init grads[1008]...\n",
      "tensorflow.py[line:72] INFO Finish TensorflowPBModel init\n",
      "base.py[line:87] INFO adversary:\n",
      "         original_label: 169\n",
      "         target_label: 651\n",
      "         is_targeted_attack: True\n",
      "deepfool.py[line:54] INFO min_=0, max_=255\n",
      "deepfool.py[line:73] INFO select label:651\n",
      "tensorflow.py[line:127] INFO Start to get _grads[169]\n",
      "tensorflow.py[line:129] INFO Finish to get _grads[169]\n",
      "tensorflow.py[line:127] INFO Start to get _grads[651]\n",
      "tensorflow.py[line:129] INFO Finish to get _grads[651]\n",
      "deepfool.py[line:121] INFO iteration=0, f[pre_label]=7.55899429321, f[target_label]=1.01285743713, f[adv_label]=7.55899429321, pre_label=169, adv_label=169\n",
      "deepfool.py[line:121] INFO iteration=1, f[pre_label]=5.69916343689, f[target_label]=1.23105657101, f[adv_label]=5.69916343689, pre_label=169, adv_label=169\n",
      "deepfool.py[line:121] INFO iteration=2, f[pre_label]=4.41417503357, f[target_label]=1.52083945274, f[adv_label]=5.93840408325, pre_label=169, adv_label=75\n",
      "deepfool.py[line:121] INFO iteration=3, f[pre_label]=3.36010456085, f[target_label]=1.94902014732, f[adv_label]=6.58306312561, pre_label=169, adv_label=75\n",
      "deepfool.py[line:121] INFO iteration=4, f[pre_label]=2.61650586128, f[target_label]=2.10217690468, f[adv_label]=6.90624427795, pre_label=169, adv_label=75\n",
      "deepfool.py[line:121] INFO iteration=5, f[pre_label]=2.34271979332, f[target_label]=2.25374174118, f[adv_label]=7.01909255981, pre_label=169, adv_label=75\n",
      "deepfool.py[line:121] INFO iteration=6, f[pre_label]=2.28898382187, f[target_label]=2.29194664955, f[adv_label]=7.04569721222, pre_label=169, adv_label=75\n",
      "deepfool.py[line:121] INFO iteration=7, f[pre_label]=2.28715276718, f[target_label]=2.29321575165, f[adv_label]=7.04657936096, pre_label=169, adv_label=75\n",
      "deepfool.py[line:121] INFO iteration=8, f[pre_label]=2.2833673954, f[target_label]=2.29580163956, f[adv_label]=7.04838371277, pre_label=169, adv_label=75\n",
      "deepfool.py[line:121] INFO iteration=9, f[pre_label]=2.27571678162, f[target_label]=2.30114865303, f[adv_label]=7.05207538605, pre_label=169, adv_label=75\n",
      "deepfool.py[line:121] INFO iteration=10, f[pre_label]=2.25991654396, f[target_label]=2.3119533062, f[adv_label]=7.05955982208, pre_label=169, adv_label=75\n",
      "deepfool.py[line:121] INFO iteration=11, f[pre_label]=2.22777533531, f[target_label]=2.33361411095, f[adv_label]=7.07517623901, pre_label=169, adv_label=75\n",
      "deepfool.py[line:121] INFO iteration=12, f[pre_label]=2.15995168686, f[target_label]=2.37396287918, f[adv_label]=7.10525798798, pre_label=169, adv_label=75\n",
      "deepfool.py[line:121] INFO iteration=13, f[pre_label]=2.02785944939, f[target_label]=2.45197582245, f[adv_label]=7.17348861694, pre_label=169, adv_label=75\n",
      "deepfool.py[line:121] INFO iteration=14, f[pre_label]=1.76480269432, f[target_label]=2.56326079369, f[adv_label]=7.28365325928, pre_label=169, adv_label=75\n",
      "deepfool.py[line:121] INFO iteration=15, f[pre_label]=1.47050833702, f[target_label]=2.71713352203, f[adv_label]=7.43582057953, pre_label=169, adv_label=75\n",
      "deepfool.py[line:121] INFO iteration=16, f[pre_label]=1.16829133034, f[target_label]=2.72454071045, f[adv_label]=7.47253608704, pre_label=169, adv_label=75\n",
      "deepfool.py[line:121] INFO iteration=17, f[pre_label]=1.01317334175, f[target_label]=3.05518889427, f[adv_label]=7.65266036987, pre_label=169, adv_label=75\n",
      "deepfool.py[line:121] INFO iteration=18, f[pre_label]=0.695157706738, f[target_label]=3.17018771172, f[adv_label]=7.61353588104, pre_label=169, adv_label=75\n",
      "deepfool.py[line:121] INFO iteration=19, f[pre_label]=0.583851099014, f[target_label]=3.60584282875, f[adv_label]=7.88780117035, pre_label=169, adv_label=75\n",
      "deepfool.py[line:121] INFO iteration=20, f[pre_label]=0.353077858686, f[target_label]=3.83535122871, f[adv_label]=7.83315944672, pre_label=169, adv_label=75\n",
      "deepfool.py[line:121] INFO iteration=21, f[pre_label]=0.34942188859, f[target_label]=4.24929571152, f[adv_label]=8.23922348022, pre_label=169, adv_label=75\n",
      "deepfool.py[line:121] INFO iteration=22, f[pre_label]=0.316306650639, f[target_label]=4.90989494324, f[adv_label]=8.1107673645, pre_label=169, adv_label=75\n",
      "deepfool.py[line:121] INFO iteration=23, f[pre_label]=0.165087118745, f[target_label]=5.52793931961, f[adv_label]=8.39105701447, pre_label=169, adv_label=75\n",
      "deepfool.py[line:121] INFO iteration=24, f[pre_label]=0.12136721611, f[target_label]=6.25620889664, f[adv_label]=8.13843727112, pre_label=169, adv_label=75\n",
      "deepfool.py[line:121] INFO iteration=25, f[pre_label]=-0.249790966511, f[target_label]=8.03675746918, f[adv_label]=8.82429027557, pre_label=169, adv_label=75\n",
      "deepfool.py[line:121] INFO iteration=26, f[pre_label]=0.590181529522, f[target_label]=9.41766548157, f[adv_label]=9.41766548157, pre_label=169, adv_label=651\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "attack success, adversarial_label=651\n",
      "fgsm attack done\n"
     ]
    }
   ],
   "source": [
    "#加载解码的图像 这里是个大坑 tf提供的imagenet预训练好的模型pb文件中 包含针对图像的预处理环节 即解码jpg文件 这部分没有梯度\n",
    "#需要直接处理解码后的数据\n",
    "image=np.array(Image.open(imagename).convert('RGB').resize((224,224))).astype(np.float32)\n",
    "\n",
    "\n",
    "orig=image.copy().astype(np.uint8) \n",
    "#[224,224,3]->[1,224,224,3]\n",
    "image=np.expand_dims(image, axis=0)\n",
    "\n",
    "session=tf.Session()\n",
    "\n",
    "def create_graph(dirname):\n",
    "    with tf.gfile.FastGFile(dirname, 'rb') as f:\n",
    "        graph_def = session.graph_def\n",
    "        graph_def.ParseFromString(f.read())\n",
    "\n",
    "        _ = tf.import_graph_def(graph_def, name='')\n",
    "\n",
    "create_graph(dirname)\n",
    "\n",
    "# 初始化参数  非常重要\n",
    "session.run(tf.global_variables_initializer())\n",
    "\n",
    "tensorlist=[n.name for n in session.graph_def.node]\n",
    "\n",
    "#输出全部tensor\n",
    "#logger.info(tensorlist)\n",
    "\n",
    "\n",
    "#获取logits\n",
    "logits=session.graph.get_tensor_by_name('softmax/logits:0')\n",
    "\n",
    "x = session.graph.get_tensor_by_name('ExpandDims:0')\n",
    "\n",
    "# advbox demo\n",
    "# 因为原始数据没有归一化  所以bounds=(0, 255)\n",
    "m = TensorflowModel(\n",
    "    session,\n",
    "    x,\n",
    "    None,\n",
    "    logits,\n",
    "    None,\n",
    "    bounds=(0, 255),\n",
    "    channel_axis=3,\n",
    "    preprocess=None)\n",
    "\n",
    "#实例化DeepFool 进行定向攻击 \n",
    "attack = DeepFoolAttack(m)\n",
    "attack_config = {\"iterations\": 100, \"overshoot\": 0.05}\n",
    "\n",
    "adversary = Adversary(image,None)\n",
    "#麦克风\n",
    "tlabel = 651\n",
    "adversary.set_target(is_targeted_attack=True, target_label=tlabel)\n",
    "\n",
    "# FGSM targeted attack\n",
    "adversary = attack(adversary, **attack_config)\n",
    "\n",
    "if adversary.is_successful():\n",
    "    print(\n",
    "        'attack success, adversarial_label=%d'\n",
    "        % (adversary.adversarial_label) )\n",
    "\n",
    "    #对抗样本保存在adversary.adversarial_example\n",
    "    adversary_image=np.copy(adversary.adversarial_example)\n",
    "    #强制类型转换 之前是float 现在要转换成int8\n",
    "\n",
    "    adv = np.array(adversary_image).astype(\"uint8\")[0]\n",
    "\n",
    " \n",
    "\n",
    "print(\"DeepFool attack done\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "l0=89493 l2=69897.6119835\n"
     ]
    },
    {
     "data": {
      "image/png": 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tLW44eiMPPfIIX/OOd3DkyFGmRY6vPMYKeCFJEpIoZrQ74tKT53jogc8hCGefOM3u7jbGEqwdF/Gh3/tdLj9xhsMbm4gVvuXb7sbnOTuXrxCXOXgFq1gxGAOly1CpqLJgaSAlriyoygqvDtEAUqqQWEs+K8J5ARvHRFYQlCIriGKL83Dd2hqKZbQzxhw+xOFDh/HOMR0fxqthNJmwcfhG1CirG2sUs5zVlZXwrAvPTjFDnGLVEBnLzJektkfSVxwe5xyCJYrC5CWO4wBWWMQKViJMZPDeARbvPJGJKcVgvCMkVtjfNGx+7wWS+mIDFHNFzaLCbQGpPTEHjbb+Aow9FRf2tN3R/q3ib4GGBizm/DUluuDX7bfb1RwghnvG3L0XXUCas8Ow03fn3s3HvHjv2vIdnodzJjp1rx1e7QuQuvczn+HosWMcPXETl7aD5VG4Cmtj7vr6u3HOk+c5tnZfiQTlM94dcfqxxznz6c9y4fHTAPgiZzVJkMaasAlMCz71Jx/n/f/s5wB48xteRdoTLj15jgMbg2D9WIuvrSNflZRljnMlqEPV45t0V2rwYhAfLJZSHUVV1TNyiFGslTCLrSo2NjbY2tpiMtrFmIiyrBgMBqRpymQ8ZWP1KFtXt4mSmMFqAKXTjz7GxuEZR06GN3pIZIjHBUVZUrlgQRupQdgYjDHBelKDjSKi+uOqAF7ee9TogqsPQJ1H0HZmv9/p3k9/hmPHb+TIjSe4vHOVJEnJqxITRdx1991UzlEUOTZNQSw2siRJymi0y+OPn+ZsLScCVHnGWpoggHolThPIHJ/6xMf5xZ//OUThLa9/BenAcPnMWdbXB1gjxGpRA3kJiqPKZzjvEF+B9/jYwKwCIzgFg+Cdw+Epq4pKq+ByU0WN4CrwONbX1rl8ZZvR7hhjLUVV0l9ZIU1SxuMJ62s9tq5cIUo3gpx45fTDj7F5aMbR216F90BkScbBqi9dFZ63KCqKGCHCElmLOsFGlriVk1DGqxJFAIqxSbCeIkVKJUIprUfkZbCMPXyqAp1bJnNl3/3eHDfgMm+oo6O7ypn5cbf9pnwX8BpLqW172P00fA7ngNR+mnodXjtluuDEwvn5GOou2nNzcG5HMB9/F2QWrnfaHba3ZT7YPdS9g9eC9gVI3X777fT6fZ44fZr16w7RXxmwvXOVd3/Xd7G6usrudIqxYebnvUedx1eOne0tHvnCg1x89BFGWyEReRQLcSIUs5CB3knJxsoakfd8/t5PA/DJP/lj3vj6V0ORMZ0WGBMsjaDsHb6scL4MLsVGoeNAa0WuBo+i4tAqzCyNMXh1tdtvPtvMsinOOZI4JU37RJEjjlLAUJUFq/0eGxsbZHlJksYAbK4dYLC2htTA2E9jBklClhX42uJZSVOKrASCsgsAZImMrS3C4HZctKAUEY8xMcYYnJbt+ZeDu+/UqdtJ+32eOP0464cOk/b7XB3t8O53fzcr/RV2ZzNMlJBECa4MkwtXOXaubPHoQ5/nwiMPM7oc5MRGEPct+WSGEcFpyXp/HVOt8PnPfAYj8Mk/+ShvfMOrocjJZgUWQ5VGqBfEKtWsQKnCvfMQRYLLHNgIIx5fGpyAGk9ReBAwavAEK7vwLphxBvIyQ70jjXskvZTYxsRpD1RwZUEy6LN5YIN8VhL1YsR5NjeuY7C6FtYa1dNPIvpxQjYrgmwS5KTMS1Q9GEHEYKKIyERYE2TeREF2RARXeWxsMQQ5ERGcKxGVmvf9v2VlPosfLv4bdqyDvXXqH8OOih0yV9yNFdKARltuSEdRN2XaC+1nodUWrOZtzr93qjX9dhBozme3307dzujanofzvhoEXQChZowL/DO/kXv6nY+oW5anqdvh70XQvgCpn3jf+/iHP/uzZFlGryiYTCbc+bo38JVf/VWc375KnPRI+3VwQlmhCFWRMd4d4bKCYjoFH4IHfOVxqngX1qQQwSqsDnpUtWvvwc/dy7Eb1un3U0rKFqCMMrc8VAFPXFtuzQekdpeBqgV1GBMFpe+FsixxzSzWGCaTCUmSMBj0MWLbIA1rLJub66RxWJvo9z2FhsehzjEZj7Er4VVWSZSw0k8pioKsBsXKKSaNA68EayqOY6IoARO1YzcGIjGIBVODVwBRszCuxrraz/Tj73sf//Af/SPyLKfIc2azKa+t5eTC9jZR3CPtpURRHCYU6nFlxng0wmUlxWyGeAcIeI/LSrwr8SoYESJgY7Uf5EmEB++7l6NHNugPemTVjF6S4KYQRxZXr4+qV8CRxDEOA14xxiDGMp+sWBSHjWIiVcoyp/QuuN6MICqMRhPSXkK/10OMwVcK6onimAMHN0gjiGzESqrMiDBG8ZVjMp3AeIKxQpqkrPQTyjwhqycdVQU2jXHeoViMBHmL4xQVG1gUsAYsEuTEmBCYoR6wmNjiS1/LycsApGCuiFulXF8b0lotXQ08PzVsFe5eMBvWPxewoQG/DizOQaYDWM33Fkk64NCUb/hoQaPzo+V73v5wob+Gr3akHR66YDjs3IO2hT3V5v004Nxcn9/TZhjDlodF2OrcpBdJ+wKkXvmKU4xGuwwGPbIsI0pi3n7XO5jmBWIMNo7ARHhX/wF55cyTZ3nkwQcZj3bJ8wlxXAdR4BEr9GwvNK6GqirI85wkCZbKdDZi++plZlnC2gGLa11mgnOutjik/mPVBUtLRMBI+CP2JixKG0NQfIIKmIq2De89g0EPVWWWzTAmwiJEBlY2NxmNt1AV+v0+qU0Df4UjigxVHqzBOOnRiy1r/R4mD1GLp584S9xLa1B0mMiSJv0A5ApV6Un6PdK0h/aVnhlgIwVT3yd1X7oHfI3oVXecYjwe0e+n5FlGFMe8/WvfwbTIwVhsEoGNcU6xxoDzPHnmDA9//vOMRzvk2RgbWYwRFA8GBv0eIYLNUJU5eZGH9TyEaTbi6vYlsknK2iFLlmWINRR5sKzVK4JgYguFYiREdGqY7QTNr4qqDXJiDaqeyKb4qsRWwdWICN5X9Ac91HnyWY4lxgIG5cDGJuPxZcQIaa9PFKXglWnpsYlFqwxXCqR9erFlddBDApM8ceEccZoEV2NVtXISJxmVU6rCk670SdMevZ6S9vsY9UgUtwCMV0Sl/rzEQvAcKOjeuUKeT/+HHUU/V9yNom6BqSnftrcITItA0dSv222KzM8u1Ju79RY66NQJR3MgmTe4MJ6FFhYgstPuHAA7t6ADmPOxtGDe8Pg0tGBxMS/b8jmkU3cPLy+C9kXGiQ/+i/frr/36b3D/Iw9z6OhxBgev49u/9z3s5CUbBw/hNMz0XVESWSEbj/jYH/wRH/3IR5hNRpjdEWhQ3l5DlF3jOa9KxZo+eGV1LQDXd37Xu3jTX3odiOPIsY0WfNRTg5TUwBQT2whjotZ1Zur3JTbWlqWoe/J1/y5EBzoXwtedY2PjAEVR4EvP5uYBBMt4PMaYCK85cZxQOiWrgjXTG6yhNsHH6+H76galJmR5xc4kANcn7vlTvAh5nlOUJdbG9Ho9JLKUpSPLS47fdIJBf5X1A5usra2T9nqY2soKLr7AX2MV3P2Kg/vanPrgL/+C/tqv/wafe+RhDt1wnJVD1/Ft3/sedvOS9YOH8fUbU1xeEkVCNhnzsd//Iz72kT9iNh0jO7tBTgS8lsEq0OCmm44revEA9crKSg+M8J3f/S7e9BV3IuI4cv06RBYbG7SCyjlwIXIuskkAqDgmshFRHIVoQqAqK2xsMD4PFnhtPWE8zgWXsisKnHdsbh6kKIOcbGwEOZmMxtg4xlUz4iih8EpeAFbo9dcgTnB2HfUw2Nwkd3EtJ8FrcM8n/xRFyPIsRB3aOFhrUURZ1XJy4gT9wQobBw6wtrZOkvYw7WRG8RpkxFUVXpV37XM5GdbvSxrWPzo6dI8l0ZwcLlgQc2sHnl7ZDp/yswsUi3DT6ZNuPywAwoJy7wBot+9uvUVOOuDTgs7TgG57L4YL/S3U3zOKtn5zOJxzNeyU75xoOoe/SCHon/zEx1Echw4d4uFHH+Hu172eyTSjVMiKApOkiAcTRySRZeY8eE8sMKsc3gi+xlpRAQkJpwCsiYmjHnmWsT3aBWD1wDp2JeHgwQ2y8eV2TaYBpyRJiaOU2AYrTqjDcsWiAlK7zQTwrqitrhgRJcKiaoOl4pOwj6nMSWyMiSOKIkdUiKMaLCrw3hFbi8TBklJ1oI5+bR1WWcba5jprg5h+L7gAt86f4eKVnWBF2eB+mhUFaZrSH6zivWc82uG22+/g+I1HGe/ucvnCBY7ffDP9fp/t7W3Sfoo6H6zPl8F6+Cfv+TgqjkOHD/PI6Uf5+je+nukso/RCVhbYJEVUsGlEYi2zXY+oJ7GGWVmh1gQ5URAkBBREhtx5eqs9IjtgNpmwPRkBsLYR5OTQ4QPkVy/iZiGSEgxRbIhtSpomRHVUJxrkRDHBjWcMkQ2us7LMMFaIB2nYk1f52nqvkDiiKkuKIiMxMabfoypz8CGgB/UIglNHHEWYOKnDwx3qPf1ehADFeMb6wXXW+hH9foYIXD53hktXruK8xxhL5R1ZkZMmKf2VNZzzjHZ3uP3UKU7ceIzxzg4XZ+e56eaT9Ff6bF/ZJuklQLOX8CUUgOdKw/nBkEaxMkcr5gq8Kdoq3BY4hgvtdBQvC4qZzum2/hwIQvGOxdFhYxEsOjx3eJtD1XCON53xzPkfLgLfcM5LF2wb8Or23Z6fo9gcsBpemvvWGXRbu7nW9t29sy+e9gVIbaytcfniJS7t7HDixAmOHz+ORJZenCBxQuUVdQ7jFXGeKi9wZYmvKnxRoEY7INW0Wgc8CMSxRSohrt19Egm5yxhlsJ5GIRoqL8myDDAMBishdDlJEBtAah4VVwch+PDbStSGu6sqlXrwwZpqmCqyjEk5AjXEtolQDHuTjFVETR0qXkfuxb0AkO1eLDC+ovKOuB7gyeM3cuHCBba3tsjzHE9oz8YJxlrKsuKGY0c5cvgQD37+fi5fvsydr3sD5WxCL7GksWEy2p1H+u0Di/rZaGN1nUsXL3NpZ4ebbjzBiRuPg7X0+ikSxVR1UI04BeOpspyqLCnzZg+TD6txYSkRrwq1a7BUxUQGrCGpJxASCXmZcXW0zYF+HAJXypLZNEOMpdcbECcJaRqjNsKIxWBrK0TwVYiGq0pFJMZGwb2neCBYsqouRKIKFNOMmR+BN1ibksQRiOCcx9oArKIOH+ZhGOkRG4O1YVtBakB8CH+Pa7vyluPHuHDxPFcuXyHPMlQMrpaTKI7I85Ibjh3jyPWH+PwDnwty8vo3UszGpImp5WSEyDO/znVf0XCuVOuvrSJeBCraC62FMJwr2C6YdAGsBZG2j+H8d6ezebmmbpe3DsC0QLT4rT1q++32VbfFsMPnnIW9bM3vy7A91QLVwv2Z87hYfc7RUy2mOfA14NW1uF4s7QuQOnr0Bl796ldybmubk696LdcfOcLWeESydiDsF1pdQ8VjvMfUVo+vSoz3xNaS+6r9A6rXgVtSDEVZUqhjtRcsFZtasjLDjfOwdoHBiNCr3WFpmmIkbIZM0wjU1KG3AZicCl5DgEWUGMDXwOXDfhlfhfB1r2EDpa9D1BWsMXhv8K4M+6iSsJdJTIRJAirFUYwRrRf5wUQxxjvEK/3a2rrjtluYjEd88k9HnHn8MabTKaV3GBsTJWENrCoLNlZX+fSf/SnOe77qrW/BVRnZBGbTjCybLET+wZEvwdN+4XT06PW89tWv5OzWNidf+RoOHznKlfGYZCMmr+UEUYw4TL1hWXxFhBIZS0EFIpSlx9qwDoU0MmPCXjjj6Q9S8GBXIvIqw00Kolm950wM/UEfMRHpIEVMhIoPLj4frgvBsvcmBMp4HIN+jMsLNOwWp1KHVlUAKueJ+waloihLvINez1A5g3cVVVUSJQapDGI8xCZMXKIEaxVRDy54GowGl1w/TjEinLrtFibjXT45+hTnTj/GeDKlrIN9oqQPAlWZs762wqc/dQ/OK1/1l9+Kr3LyyW6Qk3wKdCcy+1tOWmUKc0XevTqcK9G58q/rNRZCR5nPEaCruOcKfo5Nw04bXdhZqNjytNDOwjUWP8M91zoX91poi7dhOC/bdLDQaGfc7XjqMXT5afvJDhz0AAAgAElEQVTpAGeH//kNGD6Vh2tA+wKkrlze4qbjJzh/5SpXrlzhytWr5CZmkMTk6nEoSRKTIJiyxFUVZZ6jzpEYIV+Ing6ggdZmiBjKsgI1rK+HNZ61tZXa1eKZTIL7zEhEkiT0ej16/X6dNUIIUcJS75WS2jU4jwL09X4pVYfzFerCb1xw2WkZ9jBFkSEyMVFkUFdRFDl5XpJKhDEOMWHTJIA3BZWNkShEKIYMAY5IDL3aBXhwfY0brtukby1uNiWfjimqsLZko+BavKSO+9Vz5txZ3vgV/xHqSybjMVcvV1ze2gJryPM8pJbq9YBXfgme9gunK5evcOLG45zb2ubKlW22d66SSUw/isjV4wWSJCbWGFsHCuSzDF9VpFYoalefsQZFaYJdQpg15FUFYljf2AAPq4M+gsGrMhlndd2INEkYJClJ3Ce2Ed4bxIckDuCpVPD1pmnvPOod3glqQIwjryrEO5yWmKpEvafMLEiw6vppEiZFGlzFWVaSEmGtQYwSmRAcY7tyooCv5QQhTSLU+1pODtCPLNV0SjkdkzuPqypsFGGSGNGS+xXOnjvDV/ylN6GuYDwZc3Wr4vLlK6g15EWO+peHnOw1J+a6N5x7qpurOV5Ush1DYd7kguIf1v+HXT3dft/LyvDpeGqVfOe4AYJhU2c4LzsctsWH9fcW8IZ0ynYHNx/UsD4/XOi3c3XYKdep2t6blp9he65ts7k+XOjyRdO+AKmyKlhbX+fs2bPE4wm33Pl6Vq47QBUmiEymGaY/IIoj8J7ZbMZkNCabTqAKWShaN5/WWXNrkDJqEBMRxcKhAwcBWOn1iW1QTolNmEwmTGYjoihic1OxUUKaWEQsRVEES0dMndPM1NZUWMMq6hyB6iq8BpDy6hAfNspmWYZ4RRSKKqPIpM6X58F5qthjbYjk8j5ksijLEkdGr044IXgiHCoGqQM0prtX+cID93P+zOMUsxH92JJaw2Q2pcwrtDCUWUaRz8iLgttuOcGjDz3A1tY2u+MROzs7lBru5WAw4Pjx41+SZ/1iqKwK1jbWOHvuHPFkwsk7X8fqdZs4D16EyTRDev2wjqhKNpsx3h2Rz6ZQVmEtkXoTczCigqvV1JkWJCZKhUMb1+GdZ6U3IEkEEUcSJ0yn0xDIYC3VJmwkCWliECx5XiLiQ+qSeos0eCoX1pPyqgQP3hdobUU5PAYQGwJgbBVsuqzI0BJQT+mCC7OqPKoxkQVXVYiVsLlbMtK+1mukHqsOTIxRhwrMRjs8/MD9nD97miLbJY0MUWSZ+AJX5RR5QTnNKPKMPM+59eRxHnvoAS5fucpovMvVnV1K75jNMvqDPidOnHgJJeB50LD50VGk8BSgaksNFy2JPTi3qKwXNHvdR7ftDngNOwq9AYwFfro8tv0NF/hprbs5Ui4CRBdZ2vNNm52emjahvTZna8icieHCv7brpl57H5qRdwFqPuZFAHzhtC9A6vSjj3H0xAnW1la4+RWv4IYbbuBKUXHlyhYrBw6iVUXhKnpxiLCLbR1tJ4JTj9WgbKB2SQiYOn2EiGCAnk1YSVcAqPKCmauIE4OSgFoiiYiiJERqESLkiqLCRklQPvXGR9CwBadOd9OErHtfob4KmyY1uCVVQvi6dyVFXoa1ozotTRJFRDYEVoSNtCmmTrfkNOzFso3fUutpulaUdVj6la1LjHe2yadjXFGgQOUdZV6EdTRRppMpiuPAdYcZ9GL++CN/yKVLl5hlBarKznSMiHDkyBH6dVLV/UynH32MIzcdZ3VthZN3vIIjR47UcnKFlYMH0bKkiGJ6cYRBQvLUOCIyUKnHaHDrWWtQH6wmoxZfhIAAa5REEtbTFSoFlxdMypIkNfRNihJh1Ya9aDbGYKlKR1G5YAWLD7nvbHAhhxlTWECqigIQnC/Bh4AHFQ1h7OIx1uDKirIog9XifJDLyBKZBOeq2t2WkEYOLZXKCuLAHpR2X5MRQDxFniHA1tZFRjvbZNMRrihRlEocriiRKMKIYZbN0F3HgYPXs9KL+eM//kMuXrxMlhd4H+TEiHDk6A0Mapf5fqdh83vYUdQLJ2mBZA5Cw7pYF4jq4wVwa+rPwWIONB3AWmhzDhJN622fHRAbNjW6/TRl2t8Lg1kApS6fw07b8+OmuTnIPPV3d/zzO9pcn/PSDqzlsx3y3uovgvYFSN1zzz0cv3yZtbU1br/9drIiZzrNURMTx3E93wwutiRJ2Nzc5NDB61hbWWXqdvFV1VpSqoQ1pE4ghcWQGEsvCYo4H0+JEkdESuZD3rLVlXV6vR79fj+EhvvgMgyKQaB282GCYmmCJ4zRuevPuRqk6t8SMlY458jyGUUWrK6kSZtEQVlVWGtD2xLcfVFSbw4uA2iJJSS31eAuIrDB0cOHOHNgk0vnL7Czs8M0z1AMab+HSIRBKfMMV+bc8/E/4bHTT4QNwUWBMZZpnrG2tkZsBF82ofT7l+655xMcv3yJ9dVVbj91O7M8YzLN8SYhiuI2BB1RkjThwIFNDh08yPrqGpNqB1e5MLEoFCuCCHivGBUiEWwZ0V+L6KUxTg3ZeIpNHDEJmQhGhdXVdZJej5V+H2viEO0nFi9aw5JCHRyhhI3ZYIiMhA21vokkdah3GO/BeKwJE668mNWZRCAyoF7wPqeqEyp7BSRBVYl7MZG1uLIMQRUWyqII+8A0JJE1Khw5fIjDm5tcPnee3dEuo2kGYkn7PeIoojRQZhm+zPjkJz7Oo48/QVHmZHmBsZZpkbO+uhrGUJQv0dN/PjTsAEAHpFqlX3/bAzL1yflxA04dUNvbzvzScA4QLXAsQNTi0XARGALPi4Ax56Wj/Dtl5212+l0YW8vSQh8LY2/7acYwH2Ln0qJV2d4X5uD6FN6uHe0LkLrzztdQYYjWN+j1epw7f57kwCE21jeYTqf0V9aoqopSBIkiVlZWWF8PoDIbjYLLplmD0kaJ15YUhjiK6CUp/TTsk4qMIRKln6b4qSISLKIQfBDAaGPzIIPBgGk2CxFwnSi4+Wc+hoX0QhrWIZCQry2fzciyDHVav5Yh5CKsqoqoH7UgF8X9wB8QWSEv6vUym2JthSNkOwBYWe2T2BDk0Utjpokl9jGewENRzOj3e0xmOVuXLvKHv3+JKElYWVtnOp2GfVvG0O/1uLK1xROnT//5PuRrQHe+LsiJXd+gl/Y4e/4CycFDbK6vM51N6Q9WcVqR57DaGzDor4R9P0mPqRkh3oeJBQZxDonrRKyRwXlLFFvSKCWNwz6pJA5ZI/ppisuCE887R57PcISI0o3N64KcTKdh/5PURq8AKFhB67LGWkpXhMkMQU6q2p1XVo6imJHlGa7wxFHY0lCUJWVREvcszkV1pF8PY8NELLJQZDMwgrEpxjoqhTgyiMDqWo/UWnq9lH4aM81syFMoiveO2aSgv9JjmhVsXb7E73/ow8RpymCwSl5m5HlBJUIvitm6ssUTTz7xEkvBc6Dh/NcCWDVqfLiIOW2RzsFw2AWeYeffAnbM8apBJbptdws1vc07HO75PS867IBLt609WMkecBw2oNEd2bA9363X5bcLc08537TZVhu2Ndp/w8619t7tYfRF0L7YHXNxZ5djJ08ymk548txZ4shi1JONd4KPvSpwrsBTMdOCkS+oBin+wAZXI8EmSllVlFXFNK/ISiVZHZCsDtjJdjGrBZtHehw8tsLBYyv42CLRAEeKtY68HLM73WZajanIyHTKbrbN1ngLbxUnUHmlKJVi5nEzQcqUmD4DV2CnIxjtYmcZce7QcUE5KnDjinJcoJUniWJs5MnyHS5fPcPlndNcnTzJaHdMnucYEwV3oa/IJ2OK2ZQ0jUnTOLySJI3xccyOK9lxJQ+eP8/WZMbONGdaeiq1KAb1EqIKgWo6oy/Q855VlH5Z4LYukUzHrJQ561SUWxeYnn8SO9l5qcXgWenCzi5Hbz7JaDrlyfNnSWKL8Y2cVIgrqcqQaXymBSMtcSt93OY62xZsApWGvHmT0jEphGRtBZP2gpys5Wwc6bFxdIUDRwc4a7B2gJOUyDryasLuZJvJdJfSZ+Ruxu50myujLbxRKhSnkBdQTD3VRJAiJqoSVqRAsh10vEtU5kSZQ8cl1aRAxxXVNENzR2Jj4p6SVzts7Z7h8vZpdqdPMpmMKfIcayPQClcWFLMxVZXRGwQ5McYgSYxPY3Z9xVVX8cDZC1ye5FydFEwrpfIRqKAViCqJgWqa0QNS51iVICd+Z4toMqZfZqyLo7p6idmFJ4mmV19qMXhWGrIHnIbsUbzDBddYq1g76DPcaxUMm3PNMQtKuKk6bDgYdiBqOP/egFDbdqdOgy8Na3MAmCv+Lig14NLystBnF7TmMAQdEGzvwbAd03BecQF0mv5g3mb3FgzbNjo8XSOKrmFbL5ieePJJclUOHT2O956VlRUuj8f0Nw+QJAnOOaIowiOUpWOwssapV7ySMi9IkoRzn/tcmB0CNk1DYEUWrJDV1QGbm5vceuutXH/99W2fIiHPnq/qtELGEMfh1QXGGCrn0KKg8oRwXbEhY4CHygtS5ogIeVUrdwmuwTzPmE6nVFXI35emCWUJ09mE2WxCVZUgniiyGBNTlmF/1mg04ujRsCgdJxKsnfPhRY5OIzYcJL0V4jREUxw8sMGTqm395v1ZYd2MYKVF4fF2rb/u9yLPF+7HfqcnnniSwivX1XIyGAzYmozpbWySxCnOVSENlAhl5RgMVjl1Ry0nacLFz30u5PVLPbGkzMYZk+kUrLC2MmBjY5Pbbr6Vwwevx6SABxMHOdGqRCuHiCFJU5IoCS5ZV5GXJaUDayxWotrlDE4N5SxDrFC6HbxXEEvlYTbLyLMZZVGCMaRpHOQkmzKbTeqXMbpaTsJ+ptksZzQeceTIcQQhSoTJZMrsiTNIZHAasekgSlaI+mH99bqDG5wRpSpKpllOWRYhYKR+F1tVhXD0Vja84k2dk9CE5dAiy+p3T708cjy2yrpR0MMOMDXKtAEKFs8NGxCrz3eVdgsKzVe6gDL/0JbpKu3hoiKveRnWhYd1Q/N/DUi1nOyxhoZtP7Rtd3Cn5asZe2e8nQHMQbgzng4PCxYgnXs5v0PtuKjHtVjjxdO+AKklLek5kYS88GFLAIR9a/UmU6k3WHuP+rAR18R1qHmoVINE2JDtC493SkjxF1aQjIAkEPckLGbWKfjat7R0+EC1/j5/kSRaJ2A1IbOFLxUbIhmainW4e53Xsaozk9t6s5afj00MqIT9VqZZFpVmzTUEZAjgKk80qCNZJdQxcR204et3ZfnG/6gh+NDPc0uqKhKBOOogDkG9Yup10zZXsZrwKpKXw47e4VwxD/cozwUrAhaV7VPQpz5owKFV7J2u2h9Pz0cDdHOlT3uutV6eBoDarlvwWQSAtq29DAy717r8Nj01rM1HPu9n2PLctL1wH4cND03NZlRz/uftfJF78gJoX4BUf2WV2249xZMXL/HIJ+7hbQeu59iJm3js3HmiwSpah4CjQumUXtLj0JGjnBiNmEymXHn8cSa1VeDKkNU8jkMQwpUrl7nz4CtYX19v3+RrTMgs4JwLedMqj0S2rRfHKVInnQ0vEwx/3MZInQEgrHupKo7wRlzvPWVRMZlOmU4by8ZQXGncIx4lIqozFESxqTebKlJVYd9W/RoOVUelFdTh7SFky1NWBb7OqRbHcbAYJxNGo1EIYbaWJEnacXQtpzCOxewS5uUwK+7QYLDK7bfeUcvJJ3nbwRs4dvwmHj9/jmh1FTSqsz0YSqekNqnlZMw0m7H1hUeZlFfxpaIoySDCxilWlStXLrG5+UrWVtbrQBZFrEESi1fPLCvwzoeEx8aSRDFxkoa1IAkplrzUb2bGoBacOHwV+qp8eCOuo6SqPOPJhFmRU7oK76CqCsR5vHqwlrjOCBEnIS0SXrGuAmNCAlutIwQjT5HnAYwiQXGUVY6pXysT24hsnDGZThhNR5SzAjGWJA75BtM0qTPz13sAqVMwuWBtWxOCPtTV62svA5RqlfKwo+SHdHRvxxoYzpVuXavTxvzsYonhgg4ObXcAoFN03m/Dx3ChhebnQnuLA1kEk865BoC7QNi01rm0BwzplB12OhyyOOjhAu/D+ZdFTOwA4VP57Lb3wmlfrEndctspKu+48fjxVqHu7u6SxiErdBTF7csOy7IirxwYS2+wwsrGJjfedIK4lxL3UgYba/QGfap6M+dgMODGY0dZXRuEcN7I0u/3Q3aJKEIwCy4MXyeZrSpP4SqKoqCqCkrfvPywqkGkwGsAjQql8J7MOQovFF6Z5BVXdidMck+pBm9SJOojcR8nKZNM2d7N2yCGqqrCfq0adLJ8hjFgDPT6Mb1eEl6mqGE/VpJE3HTTTRw9epRDhw6xurpKkiTzTO0d6gZ1NIlxm5n0y4lO3naKSvfIyWiH1PYR58O2hDhGBIqipFCPRBFpf8BgdZ2bb72JuJ+SDPr0V1ZJkh5VWVK5in4y4Pixo6ysrQAWi2HQ7wU5iePgdqv5CFAAzlU4B2VVUeQ5VZGTu4pKHZWWOF/V7+wqUBtRipKXMCscBYbcC5PMcWV3ymRSUnjB2x4SDfC2B7bHNIMruwWz6Yy8KHGVYzKeMJlN2R3vkuVTjAkv2kzTiEEvwRpBtQIqoiTmxC3HOXr0Bg4dOMzKYJUkijEm5KnUOlBovs1Q62wqCqqUuQs7IAh/G8buC5XxjDRsf3RPDjsn50q3/dexFlp1O+yAQX26c9i0tAdi5oq7VfRtHx0OG+AYzlsYti3uaeOp8EcHgeZ1Oi01oDgf6/x4/r3FMOiU33vctTD33o+m74U7MS9+TSi6dk29cMqKklfefAu3vvJVfOijn+DcmTPccd1hXCRk4wkDE6FRgkn7mNiiWCSKOXLiFjYO3sCtx45S1NF82XTMdDLiK173WgBuu+Ukk91L1MYTQGshee9ZWVtnNptRlCWzrKL0Y8SG16qLCHlZEMcxvSSt92bVazv1jFKCYROyQsQpph8xujrhz+79PPff/3kee+xx+v0+g8GAjY0NbrrpBKdOneL6668nSjdAL4WNmCakagIQShJxrLAJQFFOmc4m5KVnlgcr7r777uOhz36eRx99lEuXLrVrUlEUta+Ob6i7HtWQiFBV1bV/mH+OlBcFr7rpJLe+6tW1nDzJqYOH8ImSjaesrCdoUWLTATayIRNEknDkplvYPHwDtx47Rsb/A6pkkzHTyZg3vuE1GK/cesutTEYXsaIkeNRqeK+TD5Gavf4qmZlRFCWTrKLwY2Qahw3eCkVVEsUxgzTF2giRsA9KyrBAZSIJrjhj8P0+RmJGW2M+/Wf3c/8DD/Lo44+T9lJWBytsbmxw8003c9ttt3Pk2BFsbx2vl+qweUPu8rDLXQ2pcazKRvhaCdPZlKwIciLA/fd9lofufZBHHnuMy5cuUuQlGAlWmq09BNK8UsTPI1Zr+TEmrANL7Wp9GaR4nCvodno/XFSqw4510IBEV/HWx8NO/a7rsHEntnX3gEdbrOmy87MLCF1F3oLLvIu23iIkduvN67Sstpf2glF3fB0+O7+bOuHT5XO46B7s9jRsIbFTfw8vL5L2/7RoSUvqUrNvtUlrpPWaC/XG2G45r3gX3oiLgkg3gETCTiavAWjqoAAxAlH4KMxNDNtZW2rWo1ywSo1QWy+0e+hEBPH1SwTjkN6iyemHKhaIjA3vaBJQ7+t8fCH1kTqPsYLDh+wV9bqXaojKg9oSbtertL03Ybza3odwqb5jpl7Yaha7jMzP15n92/lN04TMx/2yyTI7bFX4/EvneNgt1yjz5mQXuNo6zafTeKeHhQabL61iZ674h3TAYbgAAAtNd1vroF5bpuWn00fTZ9NHXW/BEuzCZlumw/IcLjtjno9z2OmzxcB2uPNxX0uKrmlrL5A88OAXHuINb34LP/ojP8QHfutfceLW2zD9VfpxRFwngBUJ4dV586I/BLURR06c4Ou/4RsBuHj+LFoWnLzxBgAuXTyLjYQ0ThgMQmRcFIU1pCiK2NneYjqdkuU5JgrJWW0coWIx9TqV1v79xHsiEziWlvfw2m1rBGsjPDCazHj8ibN8+t77WV/fYJpV7I6ucunyiMmsxBMznpb0ej1uudGipaPQCsnna0h2YpF635SxKbZ3gChZYTBI6zFE9Hq9ds9YURTtCxCDu7Jq1+WaNhtqFsxtrXxeLm4/L/DgQw/x+je9hR/90R/iA78Z5ET6K6QmIpbwYj4IitmJZzzJiCOLRjHXHz/B3d/wTagqly6ew+c5J288ggpcOn8GI0piIvq9frA2jEVRbBSxu32ZWS0nEsVEUQ8bWzBRSMMkljiBTCBRj5Ya9iKJYq2hqkI0Hl5I4oiZh53JhMeeOMOnPvMAm2urjKaO3Z1tLkY7XJ16yijmxCRj0Btw8nh4zUZRVuRl7aLzGl56GA9C0IeZEvcOYpMVVlYHGB/kJFmp5WRtnSIvqMoC5z2u8FRVRdJLw/pWDYSi4NWHzByqIVmvEPaXvcQy8JxoyILGnwNBZ8bf/B52q4Uv3fKLZYY0anwBGjqmw7yPbntdYGqqNO13CjQVmf8edo4WLJwuiDTXOmNr2p6zPgeveTvza0+5bcMG3OajHdIBIoZtW/P6i31cK9oXLz38nu/6Fo3ShMHaOu/5T36AD3/kI3zmvs/zhje/BbUJcW8FiWLidEDaHyBNhgaCgo3cjEES/G7iHFrlbJ99HIDx1S2kmrKy2mN9fRUIazzOOUajHSSfURQFo8mUovIYG5NXFVleEkUJq6urbG5u0uuFgIdsNv3/2XuzZkmS687vFx57rne/tXdVA41uVKPZAIcgxAUUaTZDCZTpaThjehzpZb5GfgOZpHmakfQROHoayTQmEzgUZwgTQbAbaDYaXUvXcqvqbrnGHr7owSMjIy+oxYg26bZsvOzezIxwP8c9yu/5p7uf8z9EgYfWkiAISCvNeDzGdX2MdgjDHv/in/93fPTRJ8hakSQZx8c38X2foiioqsoGKMeWB+1rtz1u3brB7Ts3GfRtsLFSdutuZ/cQgMFwh8HuHZTxmM5TAP7V//i/8NnHn3J5eclyafNkCSGQUlqS2caJYpMra3MutU5y6AUbEAM4Oz251jboP/tH/6nxwoC4M09++slnfPjd74EX4AV93CjAC3oEYQ/HE2izSS7mm5x+4NmVhlSgSmavn4E2LGeXuCaj34sZDPs4wiGOveb/cIlT5VSyZrlcIY3BwaesJWlaEcYhg96A0f4uURgQhR6lKvBwcZH4QUhaKkaDkc07pRzCoM9/+y/+ez76+GfUpWI+T7hz/yYuAUVRoilJFhmR1+fu1+/w9i2Pm7eOeeveLaKGpd0YiTKwOzzA9Rz6vRH9/bso4zGbZ+DAv/qf/jU///hTLi9mLJcL+2UPB9l8mfF8j9AP0NoGKttUItbbUTYMKq7wGm8/u3o7O7ve82Qy6SQ9XF9jGwu6ANIiBx1D2wJcd8Uw6WDIpGPA1yuLzf2NAV+L7ihvAWRypc66r1f6duX+ps4VoFuPcwMpW/e2IK8DLG2ft6RvBt8CViui25muts4/+2y+lHnifRlCftUyGg1QypCulqxmU77x4AH/9X/1zzg4OuLB19/DMRKBpfnRssZ3XbzGAEupUY5NXAfgKInQqo0RCsMA7ZQthx80XnlK2fMmXTObzaiVJoh61FKRZzVFWSNlwavXZxwdHXGwt8fNW8dkaWkZ0pUhz0uk8JkvMwYD+y01jEcUlc3XU9YFB0c32D88xvd95vM51WxGJTXFYoUyz3Eql1pq+v0he3u7ABSFQUrJoknSKI2HdJZIKZguLEjVlSJJEqqq2nInbj33hLDkts52bIvjOLiuXSF+ZbZumjIcDVDSkK1WrGZT3n3wgP/mv/xn7B/e4MHX30EgEdrHxWBkjStCgihESYWUxlIdacvT52DniYNAuA5RHKDLCsdzCHxhXdS1QkpFWZd4qmI2myNrRRDFlJUmLyRlKcnyklcv33B44yYH+3vcvnVEepkx3B2ilUbKggqPxSpnMAps3FY0IqtsmnalDTdu32Bv9xg/CplPp8wuJXmpSPIF8pmG0qWqFMPBmLfHOxhoiGhrVsUKY6DWHrW7RCqXy7nlZawrTbJKqaqy3S5sOQOx51FZlm/myXp1DU0aG0uvZOt/NZJKTZpfW6un5k4XgNZ125/JxjhvrnXkTjZvJo2S1iA3QLcGp9amt7Ja6Nng3FXdk+2aayBbA9w2CHakdPpy9UFMtj5OrmDWZDPmKzW7+DbptKfTtjua9b3NGL68ci1AajGfUlaSqN9DyZJxv8e3v/VNnn72cx4+fEiS54TC5spxjIdREuF5KBw0yiavqxu3cFXja9k6NHieh9YegSdsDAu03nRaQ1EUpGmKxkEZl7yoqZVDWVacnl3w9Okzdnd3uX//HuPxDo4IcEVAVla4OETDAZeXlzh4+H5IlhX8g3/whzx+/M9xhc9ymVDXrwEoyxLQDAaDZlWjeH06w/NCDg8PeecbD5o+WwBWUrf9nc1mJInk4tK6tF9c2BVUWZY2nkWIFqTWHn7rIOWrHn/rz181x4nFfEZZ1cT9Pko18+SDh3zx85/z8P1vkuYFoRvg6Brh+aAljpaA9Yo02uBI1Zw11XhK4noOjmi2gJVH6NkU70JAVUm7dapAlwWr5QocB6kc8kqhjKCqKt6cX/D0yRfs7b3i3v277OzsILwIz/XJ6gpfG3rDARfTGcL1cIVPUZb84d//Qx4/eYKrXKazlNq8wSiHsiowtWY4Hlq3cil59WaG6wYcHBzxta/dwxUOvvQwjkLW1v2uLCvq+ZzVsuZyNscxDufnU5arlU1tozV4Aq2auDAhGv5Cbc/CGj5DS9Ls4NnAKyoprZs9BuergFLrVcDWpUnH4E626lm7Otmq18LDZBtctiBmshbV6B6zZEsAACAASURBVFvXbRX9MiCtb3d1rw17a/g7olvj39V5pTctMF4Z2nqltxn7ZNPV9b9W9faT6WBjC5ZdkGzbXAW5yaZnV/8L/q7l3ztO/PvyFSqm8YEwoJ02iBeh7fYXljDWulU3bPXNFpXjNgHA6zjdxsUabLyY8FybsdeAEQ7GF5ao1WmCZLWxDgtrG621JZc1xrZ3Xesk0ThBOO46CtegHZtLSgvrr2CEa/vWnG06AnzXsbST2sYi2Uy8BqfZ13YdG3gsHBAB7VhsX0wTo2y36owQaLV2LrH3113XquM8Aa1zyJolo32ljRm29cy2P8b1Lh0T3gWOX4KoNUhsjPC24d4Y4i25zcfN6mnSEX/FmHd70wWSdZW24UbO5kqnn62wDvA1la+CRAfLusNudKxldKV22q51Xxn7ut/t62T78xX1XxpAwTUBKcdI7t2+gSlzPvnrH5POL/in/8U/4Td/7SEXz7/AUyUyWVAlS6gLtKzJsoSytNsUynUpgRJsigVslt0oiugPYvr9mMFggOfZFB9lWeK4gn6/j+sK9g/3iHo90syeS+EITt/M+Ksf/5TlIueTn/2Cf/OnP+KHP/wLMCGrpSRPQbh90qTg7p37CBHYYFLHZ2d8wH/0hz9gZ2ePo6MbCCFwXZd+P7Z0RFWBTZJYU9WCtNDMlyWrVcVqVdlv7kZQFFX74wp7brCYpyzmKS+fv26plxzHaV3qwX4Truu63fpbr6KUsrx163OrddurW4LXtQijuHfrBqYo+OSjvySbX/JP//N/wm9+8JCLF8/w6hKZLqmSFVQ5WkqyMqVsKLJU4FLgULCZJ3EcEYQhcRTSC2L6vQHCsQHBWZYjPEF/OMD1XPaODvDjPmlZUhkHcDk7nfPjn/yM5TLnk7/+BX/6wx/xwz/9EUrFJAvIV+A4PfKk5K3je7gEOFIg8NnbO+bv/8d/xHi0x/7RMcJ38X3YGUZooFY1daWp65KyEiSZZpZUJDPFclHZPFrYeZLnNomm6wQ4WrNKMmazlJMv3qCk3IAReiuwW0rZBPCuaY9AaUVdV9RKUWsb0Os0Z1lfhXmyXjFsVjeTjSGdtJXYsrZNnbXh3gIG1kZ3c3UNbNtGvNE3aRt0Gnel0amzAa7J1r/tvmx3vAt2kw04dobU1t4aL5ttxbY763FvxjBZg1mn/mQNoh0drd72GU9akV9muRYgZZRmMZsSRQH/7s//N54+ecTi8oLf+/7v8pvf/XsM+z1kXbJaLMnzHNMwR7cefgjL/k3jYuywZXy1tn/odV1RX0lJ4QU+URTheR69Xo/Dw0OCICLNM5QyaARBNMAYhydPvkBKRVU37AHKAAIpNXHUp66l5fczhnfeeZfvfe97xHFMmqZMpxekaUpd20y4fmCDigM/Ymdnl73dg9Yzr64VZVm3wb3z+ZLLy0tWy5SyrCnLmsCPfuk5uq7bgnMQWKeP4XDIYDCwugLLTdieSX3FSjtP4pC/+PM/58njRyyml3z/+7/L977764xGMVLmJKsVRZ5jaoWpNUorXHvK0jBDgKpsUkDhWmJejIMRilpWKCWRZZPyQlmg93sBcS8iDD36vZiD/X3CMCIpM2RltwT9wRDHODx5+gVKSfKqxnE9lDYYBLU2RL0+tbIBslopvvHuO/zmb/0H9KIe6TJlen5BssopyxKlNEHk0B/08IOY8WiH/Z09pKzRSlFVmrJhOcnzjMV8wfTykuU8pShqqromCAL7V978TdgUV4IwDAnDkMAPGA4HDEdD+v0+URwTRfa6KwS+73Y8Qzsu/l+FMmFjcCcbU782qB3I6eLU5vraoDfvN6uYTfsNQGzqbePTLxv8RkKrbyO/U299rVNpDRR026zHNNnc3wDRdn8342l7uwVO7TNYy+3K2qhr+9cFp0mnXVf+l1GuBUj1+hFlWXJxdg7Aj3/8Y46ODnj+xTPSNMX3PLIs4/LijMVsSl6kFEVBmds/5lqrdiUhG+81ZTTKNNekJG/SZRRF0Xq5SSnba0VRNKudPlpr0iRDa1guE4QQeG7AxcUUjEAIF1f4FpzimPOzy+bsS1MUBV//+tcZDAb8zu/8Dj/4wQ94+PBhm1qk1+sRxQGO41AUBVpDFPboDwfkVU1eWXBKkoTxeJfxeBchBM+fP+fzzz/n0aNHPHr0iBcvXrSgtl5B+b7f6uj1erzzzju8/fbb3Llzh8PDQ5s7qgn0/SqupOJ+RFGWXJyeYQz81Y9/zOHhAS+ePSfNEjzXI8tTLqfnLFYz8jKjXGcnLkuqZgVpUBjPwRiNNBqNxhiN0pI8y8jSjLK0qVWMMdR5RZpk5FlBkRUIXHrxwOYJS3OkdliuEjC088Roh9B1cfEwGEI/5PJyiut5KK3Jyop33vkag96A3/n+b/NHP/gBHzx8yGBnRBxHjIc9wshHeII8s6umqDegPxiQy5pC1WRpSpomjMc7jIa7COHy4vkzPv/8cx4/esTjR495/vIFqpvrDPCD0M6Tfp847vHOu+/y9oO3uXvnDkeHBwwHg5ZoWUmFEN05cv3nyYQOCEw6Rvyq4WZTadJt3zHS268bQ92CVKdNV+cW4LTA0OlMq72tuLUS2YjYUroB2r9NR3cck42czWjXF7akN+DWkd8ZYzvWtt2mTfu5BcB2WFvvf9VyLVzQ//Ef/yNjjCF0BavFjPFwyJ3bNzm6cczv/t73+eu/+ZQXby74+NFjPvjebzE+vE2qNIWG4e4ecb2Auvnj0YZQeIRY9gZZzckWzxBass415RkfV3i4rs/s8pyqtN96+70ROD4//snH/PivPqasJEWlcKOAqBczm8/5oz/6I77zG78OgOMKRiNr9AO/h9ZQV5rd0T4H+0d4no+D4dmzp/zoR/+Ov/jRn/Hi+RO0qfF8y9H39b09vv3tD/jWB++RFjMAkjzD9UKi6Lh5QhGX85wnT57w6s0JAJ/94m8IRby11RfHMXt7e+zsWEAEqKqidaxQSrFcLVquP6peSx9ljOH04sW1tkD/+B/aeRK4gmQ5YzwYcuf2DY5u3OB3f//7fPSzT3l2esEnTx/z3re/x96NO6RGkysY7uzRk0uMtGSuRhsiIQipcByQ5ZR88dzOESPRyuArD9fzCfyA2eKCPLOr2F4wQgQeP/74p/zVX/6MvCiptAHPJ+7FXM7m/OAH/wnf/a3fsF8GhMNobGm9Aq+HxqEqDQejAw72jnA8F4Hh5bMn/MX//iP+7b/9M569eIIQsglAlrxztM8HH3zABw/fQzJHS80ySXG8iP7gGIOD44RcXOY8ffyYV6evcAR8+tnfELh9y82nFKb5YrVzsMfeeEQUR3ZlWeQ2y7UQ1NLOkyRJKPISZIysFUpZkD+7fHmt58mESeuCvjHmzZv1y2RjwNcf1tW2AIdNva6sLeDZkrPRY8WuG3V1b+R3Df9VoNoCwrXe9e9JW6U7kA3gdK5dGQVbjTsPYtLK7zTnyoVu/fZ13ctJC1YNsP//xwX91atXfPjhhzz8xjt88eQRn/z0p3z2i08ZjUYkWconnz9isHvIF0+e8L3f+wOmswukGzLYO6AuK0LHadxlaTKUetYYYQ+KtQatFW5zkOy6LsIRGGPdsXEqhBBUVclsfsH5+TlZllFWkrg/olSSPLNu7NPpFHDo93sNaSiWniYM8b2Y5dJu0fXizG7j7eyyv7/Lgwdv8Rvf/Q7PvvgFi+WUui5RuuZAWBd8qRWltPFRvu+jG6odACmNja8qclYr64LueB7JMmE8HjMej1sXdNgwXMdxzM7OCM/zqOuaxWJBXmStd58feC0/4Ho1dp3Lq1cnfPjhh3zzG9/giyef88lPf8bPP/8bxsMRSZbwyaNHDPYOefyLJ3z3t/+A2WpKic9o75CqrIiEnScGyxDhCBekTZGu1Jo+SiGMve/7AY4jUNrBMQIhDEK4SFMxu5xy+uaSJM2oypowHFHWkmRV4noOi/klRmoGoz611Ag0jnYIg5DAj1nKjCRN6MV9RuMddse77O3t8tZb9/n1v/dtnjz+jKJaUGQFUtXciAS9QR9cRZrYPntRiNGCsqyhSU9SljVpUbBMOvNksWQ8HjPc2UUIxxLlmnWAt0M/7hGO7ZltXdUslquGE9B6PgZhE7CuDFpfa3xqyqR9XYPJGhy2bfQaLOzFrdXB+l3za10frhjljaYNsEy273ahrKuP9ftJCy8b8Gv72+3/GhgmG2EdUNtsw3X63hlrV1cLgJshsLXK3KrX6caWvm0lW9t97f1fvVwLkLp79y5aax4/fcKbkxOMMYRhyKtXr/iTP/kTwv6A8XHG3bt3iYKQrKjQjuXik0qCK3A8G5jqagcc0aacVzhUtQIpcYU1xLHXs2dHUhJFIZWU9HxBWWkuLi6YzaY4TuOi69qUCkrb4F7XtVtqOzu7rNKEIl8ipUZrgXByzs+nmNohCvscH99Aa00YhRwfH7O7N+Jb77/LfHHBy5cvePXqJQ92R0hVsFrNoeEfDOMIjUuysiCVZgVVJahrRZ5bJ4A4jum7Pfb399nf3ycMw3ZLRgh7/rC7u4tSdRtE3Ov1CKOAIAg4OztDV8LmP3Jovciuc7l77y4azeOnjzl9dWK30cKIk1ev+Zf/w7/Ej/vsJDl379yx86QscYX1jNNK2XmCh+M7dp7goG1UFcpxqaRGSWlDHVxB6PUwypK1xnFELSVxLCgqzfnpBYvZFGMkwndxIwdqjTYS1w0RrkcUheyO91ikK4psicag3BQ3LTk7u0TXDnHY5/jGMRhDFMfEt26wtz/iWw/fZZVc8vzkhNcnL/ja3pDSlMwuLjAIHBfL1u96rJY14JAmNZV0qWpFltudhF7co+/02Ds44OBwn9APLM+kA66wOUH2dndQqqIoC8q6Jo5jboY38YOQ8/MzTOXieQLpGBxz/edJ91v/5ot/x4C3BnqyAahJByjW9TsGuWOloVNvrapV3ci8qnctZ1P/CnC1byZX33YAbD2Aja62Jx3ZneG3rbe247pD6ehbbxuu9XSfBVvvO4+jbdfoWgPhZqC/crkWZ1Lz+Zzz83OePXvGixcvOD8/p6oqwjBsYovg+dMv+IP/8PfJ0hWDfkw/DJB5TiAcNALhBs1PiHBDjGMJPXE8Gzxb19S1dUrAOK2nG64N5gxDm+VuvppTVCV+GOCFAXWT+mO9+njvvffY2ztgMBjRjwdIqfH9kF48II57DPoj9vb22N/fZzzebZw2arSGMIw4ODji1s07HB0dMx7vUiuD44aEvT7KGJQxFFWN0pCXJXlZcnZxwSrJUMax7svCJQxiXNelKAoWi4VNX+44eJ5HURScnp5yfn6O1pr9/X3u3bvHvXv3ODw8xHVdlsslymgLUK5AeNffkWI+W3B2dmHnyfOXXJyfU5YVURSS5zZw+fmTL/iD3/8DsnTJoBfT8wPqoiBwHbQR4AY4jofwQlw/atK7OxjtkVeKsq6RTdoVtJ0nVVFjcBCuII4jhGNYzGbkZUHUj3B9n7IoEa6l70I7vP/wfQ4Ojhj27Typa42HTy/sEwYRw+GI/f099vb2GQx2UVpTljVKGcJezPHxDW7dvMPhwSGj0Q5podGVR288Qhlj+2gkShmyoiQvC86mU5K8QCEwwrXJFP0Iz3Mpipz5Yk6SZxgBnu+RFyWnp6dcnJ0hpWJvd597d+/x1r17HBwe4TXzRBuNbngF10k1r3WZbMxrdyXQAtTa0Hcs7fpeF3XWb7dWD6zBoYW9DphsrrUg2BXUvu3UnGzd+FtA4Spg0YJIq2dLxfbnTR83QEI71smmTjv2DehthrdW3ujsDLvzpLf7t3X1VyvXYsY9efKEi4sLm7ZCSRaLBdPpFCltqowkSUjTFFdAmaeYukZoZb/xaolNeOrjuD54vt3uE4H9cT1cL7R05c2PaWM/DHVdIpWiVpXN71PXGBuwgpQVSqmWA09LwwcffMigPyIKB4xH+8xmC1zhs79/wPHxTe7du8/bb3+N4XBsmSxqReCHNpWGH1GVEik1w8GY+/cfINwAzw8RfkQloZKQFSWrNGOV5qzSnFevXvP67LQFbIC6SSOyzuq7XC5ZrVbkeY5SlrV6PB5zcHBAv9/n8vKSn/zkJ3z00UekacqtW7fQjmnSSihqff0De588tfMkzzMqLVnMF8yml9S1pKorVqsVWZbgu1DmGUZJPEcjVI2japQ2CM/OEyM8HOFhnADtBIg4wHEDm7oFt1lhAY6DcKGWJbWWVLKk1BVZXaOQDc1UZTn+HBdHOBil+da3PiTqj/CjPuPRPovZAs/12d9r5slty3I+HI9ZLGaWa9Hz6cUxfhCS5zW1NAz7I7729tt4QYAXR7huSFVBXRnSrCQpcpK8YJkUvHr1ijevX1EVuY1vMoZaScqqpiwKksWK1WJFsliSZ3aeuK7DaGfM0cEhg1GPy9mUn/z1T/j4o49Ik4QbRzdQAiQKhaZuznWvdZk0LxO2DG1rXBsDvTHa3Yqd1QQdgNqI7axQuk0nbY3u1tq6Qfu2/b1t3v8WPNseRKc/3W3ADchtJHdBun3XXJt05G1wadO79VA2q83tbsBka3xX27eI2nluv2rxvjRJv0Lp9XqUVU66WlBXJV7gEwYe/X6fJEtxDHztwdt8/vNPuf/eQ+q6wPFChv2YvC4gGoJjgcTGxDsIt0kn70XE/QEYiavt9pkljHUQwkNS2L14rdBaMhwP2dkbc34+Q6qKMLJytNbs7u6zt3tgD9ODEN8PCIMeQRACgrpSGGNaT7/VasXueIeyLK2DvKkx2uD7IVHUo6oqjm/dJi8zsssc4doxBEEPB5dKTgGYzhZUMkF4LmVlwaQocwbexs18nW/L87zWceLi4oLxeMjNmzc5PDzk+PiYFy+f8/LlS6bTKYPBoGXcqKpt1/zrWHpxj7LMSJcVdVXghh6h79Ef9EnfpLg4vP3WA37x6d/w1jffpy5zECHD0YhClphwgHBDmysJrOu5E+D5UMuQ4e4uqwuF5yjcJvkfpklq6Rg837errapmvDtgNxlzeTFnvigJQvunpLViPD5gb7iP43oEXkDge/hRDz8I0MppPArBdT2UlCyTlP39XaqqwhUGbWp75uTZreWqrDi+cZu8zjk/y/BDGzPnex6u41JUM4QLs8WSsk4QvkdeStCGvMwZ+iGD4ZBBM0/wBZ7nsrO7Txzf4eL0nNFoxI1bNzk6vMHx0REvXrzg5OQlSTalF/cpREmWJ1Tl9Z8nmxVB82FCCyRbq6XJtsG1lyadnw54TDZAtyV3fW/S1dWVeaVNB67aHrYGf61z0tboyugCTauvI7crr6tpA0prEFrX3Qxg8ktS1u83fWj7uu7Dut6kc2/SlfDllGsBUkIIgiAgcD106CPLEozg8PCQuDdAGs3+3g7L+QJkzWA4wng+rjBINLKTuNDBtZHynmULd02EH/aoqwxT2v30ulI4RmC0RqJxA58qL3B9j9u3b2NwyQrJKisRQjRZbz2+853vIKUkimJc4VPXNe+//wFhGBJH/eZsysH3Q/r9Pr1ezzJKN67pQkAvDgiCPoCNdwp9xMpjsZpZMlQA4RGEEb3YEuIKP2B+PsML/BZMlFIUdWFjXQK7LbnekowaYL179y5BEFAUBXt7ezx8+JD7D97i9PSUy8tLzi5t/NXp6SmLxeL/tf/vv2sRoonv8X1U7aOqCozg6PCIuDdEGcX+/i7L+RLqmsFwCH6AcG1ognRszI/rCYy0DBSuHwIGzwmoCQkHMbrIwfEoC4WDY5NcYvB8D1cpwtjn1s3beGFAUT5iWZR4wqUsa4Tr8Z3vfofKSIZuhCcCKlnx/re+RRiE9OI+Siq0cgiCkP5wQL8/aBwZNGmW4/kOURgQhn1Ld1QogjhALOcsohipLPODKzyCXsQgqsEDx/OZn83xfL/ldDRGU+Q5URBs5knp4CqI4gC0PesLgoA8L9g/POCbD7/Fvfv3OT875fLiws6TiymnZ29Yius/T2DbcG79m7Blhls73TXbV4GqMcpsmd9u246EVv627E3NNVA0VSYbgGvBqnNtG0ia9us+/TKctEIn67F3ZXZqb/Vvsmm3eWbrcW9hVFu/+zTWz2zr/mS7za9SrgVIBaGHlBJV1eyMhwSui+sIevGA6eWccNDj9csTHh4c8vKLp3z43e/i+AGLNGEY9Zg36QU2RbTZCIXr44gABx9p7GFyUUs8Y7nuClUTBA5lVWEc2N3fI6sq4pcnBKFHrRRlWTHe2ec73/kOZVmyu7vXnuuMR/bcKQxjBgOfNM1wmoDeIAi4PL9gNBoQ9yKqqmA+W+J6DkI4jIY7VMmMOO4T9YYsksT2OcsYDK3XHsBgMECpC3RlWloaz/OgUmRZhpSyDUYOgqDZ9sqaz14Tj6UZjUbgGMbjMaPRiJt3NCcnJ/i+SxBci6nwf1mCyEPWNaqSjMcDQt9DOA5x3Ofyck7U7/H65IRvHhxy8uwpH/7md8H1WSYpo16PmTZ2njRB2AYHY4Q9kxM+jhuC8VAamxhRSzzjoI2hUBLfh7wo0cqws7tDaWqifkgwc6mkZWkY9g/5zgcfUtUlvtjF9VyKrGQ83MMYReBHeCMfL0sxNaAhCHzO3pyztzeyjjxVwSLPEK6D68B4tEOZzol7fXr9EfNVYhki0oyB3mF3OEa5mt5giFaX1MJyGAnHAeGBkmRJRq0kvusR9/uEfmjnSZrR6/UJI5e8yNEYdscjtLGv4+GQW3c0L1+dEISCs7PrP0+2UKBzbWOc7Wt3RbFZnWzZW7rwtYGxLqBdrTnZ1r8lbLJZZLVA0unfGhQn26DTgk0HtDbA1pG3HsOkCzDdf90H1O37pPt207ZFo+3n1B3WZLt12/bLLNfiTEoIYYN2fZ+yLEnTrGVNGI1G+MJlf2+XKs/wHTh5+pTzkxNcLQmFxnc0RZZRZBmqqqFJia01YFyEF1BLTV5K8lKSpTnzxYq6VkilmS+WlLWkKiXLNGF/f59f+/aHPHjwACkrPF9YBvQsw/M8kiRhOZ+zt7PTbu+VZclisWj3+eu6JE1XOI5lNE+ShLquW3aLNaWRH4VIbZp7PaKox8HRIUEQ8PLlS16+fMnu7j5lWZKnGVVR2J+qsm7FQ8sUMBgMCMMQKSXL5ZLpdNqujrIs4+nTpzx+/JiLiwvqhokg7oXcf3CP3/zeb/C73//t/28nwf+DInBIkhTP92yA7SojCiPCqJknjrDzpMjwmnlycXKC6yhCoQmEXVXkaYqq6zYJoVYGjE09X9eW3TwvJekqZ75MLNO+UsyXK2ppmdGzKudg74Bf++BDHjx4GylrXE9w6/YRRVnguh5JlrKcz9kdjwGNH/hUdc1iukRVNs9ULSuSNEF4UBYVWZ5RS0kURfieh1KWxSQIQ6Q0FFlpv9REfQ6PDvFdnxevXnLy6hUH+wdUVUW6TKnynCLLKYuK3d0dRuMRg8GAwWBIFIbUtWS1WDKbzVgsF6ANRZbzxZOnPP78MZcX55axIgqJeiH379/ju9/9asyT1nh3jKq9ur69uTChe2+yuT3pGuRJt3G7SmvldupP1he22qzrTlqlW3onG+1bK7arwNR531lubYvu6NwCkm7drQqbfm9v121A9CpgrZ/vGtDWstb92NLxJRTvS5b3dyr3HzzgxfPn9MKIO7dvIsuK+XzGYrGwDAmuwA18POEgjEboGmRJnRoyWWEGMeORXXVE4QAhPKrGx8BoRbYy1ArLFg0YqRDaQRsX4djtmKqyHniusRH54/GYO2/dJe4PyfKa/f1DXNehrHKEsIkQ0zRlkSyI4xjf91tOPF1L4jgmDAP29m36DRtUa5MRKl1jDIRhSFbauKU47rdblqvVCiUdTk9PAXAIONjbo6qK1sEhjNwt5ow1V5/v+/R6EXFsvf/yPG+Dd+M4ZjgfMB6P2dvbY+/oCN/3CUPr2HHdy1tvP+Dli+f0wpg7N28g64rZfMZ8tiDwfBzPaeeJqzWOqtG6QqYr0qrADHuMRyFGG+JogCM8qrohp1WSWkEtQZaWINagbcI/5SAcj6q2MUtaWbd2z9WMxyNu3btDrz8kKWr292xup6LI7DxxHLI0YZksiKKeTYxpFJXRaC0xWhHGMfv7+2ilqWQJRiGlg9I2vCKKYvJlhusJesN+mzl3tUrQ2uHN+RsbNWkC9sc7VHVBbWxuqDD2kFKDZx1/qqrCpJt50uv1EAjyxlmnyAtmcUh/NWZnNGJvf4/9gyP8ICAMQoaj6z9PWvu9saT2eudS16i3Rhg6W3xXsGZdZ9JZLUxaqV0Mu/Jmwtrsr2VsQGDSAukVKZvO0W3TkdbpZ3cJ2ErfNG+fQXf1ttHUkdWOr/OMth5XR2g7vEnn7aaXX2a5FiBVliW3b99m2OsjBM35jUvgeQyHQ+q6plaSLEmZinOUkgzHI3qDPkJWBEOJ1xjvusiQtUGv2SWEpmo41PzQsjBoaXPxOBTUvqLIa6RUIGxApNIOxnHtH/CRS1EqorCHVNabrt8bIgSUZU6SJLiu29INKaVQjkQIhyD08H23OXOzJLNGSy4vraOC57lNFl1LUbSOgZrPp9awNAf8YRhwcLDHfD4nzWyOKbdJtbDOwuu6LmEYWtqlyHL0vXnzhtFowGAwYDQaEccxUtWNh1zOMkkIw9Dmx7oGzCP/d6UqS+7eukO/bw11WRUI4RG4LsPRyJLnqtrOE/ccqWqG4xFiOAA/IBjUuNquYOsipa6b9H7G4AmDVDWOK/CjEFwHnRTkVYExJdLVlIWloTKOoBIVdQoIl36vh+969HNFFPWRsqIqCwa9AY4xlEXOapkgHI+452E0KGUTFnrxgDDy8FwHLwyItGsDjrVkenlBpWoELkrblBxCCLI0B8cwn80szZMCHIiDkMPjfaazOTpbgAOeY3CFi5IaU0uE6xFGAf24TxD5+J7H2fkpw3JgEzeOhvTimKqsuTi7IE8zlouVXdkFZmc/WQAAIABJREFUHqq+/nFSE2gt8dZWWWOM16a+rdwa70kroWPut65sEKxtvBHTgtEvG/2N5PWbzo1Jt+3WIDr96Y6uC1nbPWzldcCwrdcFNmifRTusth9dIGwFbgHSFhBNtnVsPd8voVyL7b51TJQRDnVdN9/yem2mWWMMWtXIqqDKUqp8RZ0n1OmKbDVn9uYEUxWYqiBAEfs+o/6AUX9Av9+n3x8234Q1Va3J0py8sDx5i1VGkpXUSuM49vD7zfkZr1+/5vLynNliRlnm1LIkSVbk+QpH2NQIeZ4RBBaIuj+eLxCuZu3Grk2F6zl4vmiYH+YsFnPyvMkKvFqwXC6pqqrN3FvXNcc3Djm+ccj+7pijvT0OdnbYHQ7YHQ4IXdECoxCC0WjEnTt3uHfvHnt79szs3XffteDfcPatHTjWOaiKMqOs7NhU4/l4nUtVV3hhgHZA1hWe59PvxQi3M09kjSybeVIkyCKlSldkyZzp2StMmWPKHB9FHASM+gPGgyH9Xo/BYIAgoNJQ14o0zcnLmqKWLJYZSV5QKYXrONRJxdn83M6T6QWXyxllnaF0YedJscIJbcB5lmQEQYDnuoShR+C7+K6H57sIV9usaLpG6xrPc3B9Fylr5osFs9mcvMiRZcUqWbJYLqnriqq0W4NVWXPz5iE3bhyytz/mcGePw9EOu4MBe6MhoRAEjSONIwTjnRG3797m7r177O/befLeO+9y5/YtRuMhgRdglNmaJ2WZU1UZdV2izfUPVWiN/tpQT2hfu3W2VgNrA9zWnbTVuiua7dWZfd+KaYGvC3EdRa2Rn2xWOZPN5+17k47uTcc341mPsTO2tntrwLg6zs7HzhBaMO08hw1gsekr3WuT7RtXxjq5cu9XKd6XJulXKLdv3+bZF1+QJAlvv3WPO7dvc3l2zsvnL1gsFvR7PVzXIQp94jAgFAJHSco0QRtFmhvu3rwPQNAb4gc+WWWNbpYmeG7A5XzB6y+eA6CyjMiPAMG8SJutsMiSeZY1aZK31ELagOsZpKxYLufMZ1P29/eJ4z5lVXDj1i3CMMR1Xaq6sAGjRpJlCVmWMB4NcWu3TUq4WiyZz+eboEityVYJ88spfpO8ZzwYWq89bwRAVRr2dsf04pA07QFwfvEGpd2W2Xw0GjEcDptzKdG6o7uuZaFY1wtCH6VU436eoF3RBDRf/2DeW7fv8PyLpyRJyoO7d7l7186T589eYMyCfmznSRwFNo27K0B25klmuHfzPlobwmaepEWFIxyyNEU4PpfzBafPn9lt1KKkF4QwcpinKZWqiIIQIVzyomKVJg3hsGO3ioWhKkqSZM5iPmNvtYc3GlOqkuMbNy1Q+S6gUY6DEJI0WVKUCf24T+17COGilSadLVgsFjiewDEGozXZImE5nRF4AjDsDAeEQYjvDnGEQ5lrdndHxHHIThIjXIfT0zcoBME4wPN8xuMRw8GQXhhR1w7CcXA9D9cHB+tlG/o+XuyjpGQ4HJKlCYHrYDwX17n+82QNUJt1QXN90jHIdAwuV7BgXa+V05W0vUbYkk+n7uQKgHQN/mRb3lpnq7uLJmtjvwaFDjhsPk62tixbba2sCZuXzhg2yLi1wtwaX9uPrUF37jfy2n5eqfsllGsBUvP5nPl8zsXFBUWasJjPGfVt/E+WpU1wrYesahb1lCxdWoeBYc9uV2nJ8tKe38hKsbt3k4P9AwCKYcCf/fBHPHr0iNMTC1K+NASOz6y/QPmgVI2SBikVeW4Ps6KePWcqigKwZwxSagZnA46OjpqUGAFHR0c4wjpH6FTalCBVTdEwby/ml5bXz/fxPBsX4wiDNpIsTwihCchdoGWzRekKPOHiuRa0KiOtccSAsSA1cx2iuE8YWkbr9XahBdyQ0WjEbDZjPB62sVS9Xo8sT9styqPDw9ZB5avAJLCYzVjM5pxfXFAkKxaLOaPhgNF4SJYkGEeBY+fJsp6SrRY2VcnYPiffSJaXbzAGVG3nyeHBIRjIBz5//r/+iEePP+fs9QuM1gRK4AmPXm+O8QCj0H2D0oqkrHCFIIhiGz+V5zjGoaoyzs8VcTjgcP+I0I+IAo/DwyNcz0HWFYmROHVBlVes8oJalcR+QBgG+J5vt+eURPiaGkVWpXae1AXLxcLmh1I2z5Pn2a1CB4fKUXaeOA4OPYwGz4Uo7BPFEVEY4QBFkVMXNXEcMBiMWK4WDL0ew/GAQX/AYNBjkGWsVitc1+XGjSOi0CcIo6/EPNnettusGDZLhMmm7rrWpAM/TYNJ23ADbmuD3K4kWgXbYNduk3Vkr3Vd7cYv9aYFla6Ozr3NQDcgBbB1j1bvliw2IjdYONluM1nLnmyJXD+HLWC8AmpckfVllGsBUh999BG6STvx+PFjnj55wq+9/y3u33sLKWsW8ylRFOE664SBGlkV+O4Rse+h64LTk5cAxIMc8Ng5OARsctQkSZjPFsxm1ttNlNIS03oh/jBCCBiNKqIooKhyPN/FDzcOCVLaLUJj7N785eVNhsMhcWzPqVA07A8ZSbKizHPS1LI/VEWJQVFVFb7vszPabUErTVMWqyXTy0sW83nLKOEIGwcV9uw2Te1qsqJkuZpTNwn8xoM+QX8Xz/MQQrT0Sxv+PsF7771HGNqYmfXWojaqPX9KV0tkVeL7/lfiTOqvP/4YpTRSKR4/ecKTJ0/4tW8186SWLBZTgjDAd4VNdKgUsirwXIeomSevX7xEeA7xssBxPHYPDzGOgzCwTFPm8wXTyzkOIGptKZW8kGAY4wgYj0b0Ap+kzglCF89XKOE0bPKKPMvR0iEMIm5e3mAwGtKL+2hdY2pLeZWnGWmS2MDkdEVeFNRVCaom1zWhCNgZ7RGHEa7rkixSFsmS6fmFnSd5aRkuhIOsFYPYxnpVhSKrKxaLKbLKcHDYGffwB7v4wscRDkpptJLgOODarcVvvPMuYehTVxVFlVPPKrSWTUZfw2q5oAoCwqiwnpDXvGy2rCZbxtnetL8m/G12dHsl0QW7bfCgNfyT7v2ubraadoz+unFXnxU6YW34N+1aaZNNnXZlZDvZ6fv6Y1cHrdxN+23dax3bq8YONq4H0nkc3XFsgHLzcK6C269SrgVIJUnC9PKSXq/HeLxLVebMZjOiICTPM16dvODW8Q0Oj3aJomFD4ulSVQWrFXi7PbLUxhit0pr5Iudf//DfAPD85TP++I9/wKPPPyXLMgDK+QqjoBfW5MmCOA4xDhSVT12X+IFnz5K0xnVdpJRkeY6SsFgsOD8/ZzCw22tv3rzBdV2UqkmShNVqSZElzaomx2jN6ekpjx8/Js9z7t97i2984xvcuXMP13X5+aNHvDl9xWI5a59HFEX0ohjft1srvu8jq5R0uQLswfXbX7tPVrs2T1JV4bouOzs7+L6PUjVlWfLs2TMOD/fbgN84tltA6zMvjP0mvj7buu4lWSXMphfEvR7jnT3KLGU2mxOFEUWecvLyJcc3b3B8tEvgDwGJ63tIWZIsl3i7MUWRYgysEsl8nvM///DPwMCLk2f88T/8AY8f/YyisLnEqkUCGqJQUqyWNkjagTL0KYuicSSwXH+e51LLmjTLUcphVaw4vzynN95h3C8JT1/jNWdNq+WKNFlRFImlAitzjFKcnp3y+MkTsqzg/lvNPLl1D9dbz5MTFqsZBksgHMaRzSwcuGBsvNVympKtEhxHg4Gvff0BiXQps9Ke6fkew8EAz/NRqqLMS148f8b+wZ5lUfECojjGdQ1JkiOlnSe+79nUM/71nyetdZ10PrdwsoGhSbfuuur6fRdsJmsjz9aKql3JbKR15Nmf1vB35LVyOxDZYNAWvkzY6Gt73dE/6bRvV0lbz6Azni70tIC4Aa9N+7Xyzni2+ritvwtGk47eziB/5XItQKoXhOjhiKIoSFRiVzSyZp4ljHdG3Ou/w9nZGe/d/Q6LxYzFdMqLx48wxvD+++/z9cEO9w53ABjujJnPlhTuJQAyzHj58cfMn73CpNbAZ7lhZ2ePUkqiosKTNUk+ZdGs5oTvMRqN6A8GrPKEStoVSlYWzE9/jqc89kd7BI5LltqYpfl8znI1t8kZL89ZrZZ4nscXz57w9Oljy1qgKr75a+9hAg2h4pNHH5PmU07PXlIUBUdHRwBUhUQYn0G/8UYkJymX7BzuNES4cHo5J18lGGMYDAb04x5xAGk650HDKtHr9wgCCALd0CbZLamdvosQPnkdtwHAdX39HSf6YYjsj6jrkuV8Sa9vGeznyYrxzpj7777D6zdnvH/r28yXc2azKS8ef47R8MG3HvK14Q739ndwhEN/NGIxW5GLSxxAhRknH33E4ulrdKIx2pDmht2dXYqqJior/LpmlV4w0zYPtAg8RsMhvf6ApEgopU35kuYFs9ef4lYu+4N9hkeOnSdlyXy5YLmYkZc5FxenrFZLfN/ni2ePefr0MWhFpSXvffguKpQ4seSTJx+TFlPenL6kKHJu3riBAcrczpNhz84JZTLSetXMkwAHhzcXc4okQWkYDPr0IpfYM2TFnPv33+LszSm9XkwQOfi+wfcdHHKE47A7cHFEQFHHDAZ9vCBAfgXmyca+Tuga88mkY1gntIa6rT3pGt3J5l8LFFcUNO8nndq2Jb90bYM86zprXZPOrclmRbL+3eqdbGS0nzdjbFdJ/FKVDgZNOp8nG53rPkzWoPZ/8sw6z6iVuX7feVab8X855VqAVFEUrSu0UgrPsw4BSimm0ym3bt0iSRKePHnC8fEhQggePnzIcrlkf98Gul5eWlBaZSnDwZhXr14BMJsvwXnOdDrdbKc1uZe0tu7H1q2YhixUUWvFcrkkLwqbV6qurBNCYFcc5+fnvHz5kl6vx8nJCXVdc3Z2xsXlGUmScHFxxllDCBtG1hV8OOxzdHzAzs4OSilms5n9Ft2sgtbs5UATNxW3YyjLkps3bzIej5HSelednJww6vXbTMJrWqQ4tokQfd/n5MQmSIzjiOPjY46PbZDw+vzv3tvfaNPIK3X9iUOLvKAXhmjXRyLxGtd/rTXTy0tu3b7FsB/z9OkTjo4OETi838yTvf19yqLi8vIS4cAyTxnGI16/fgUGposl8JzL+ZSqrjDaNM4uYIRlnVBaW0cUoZFSU+WlPc8qC5IkpVISV7j4gd1aOz+/4NXL5/SjHtmLEypdc356yvn0jDRLuLg84+zVawpV4/c93rw6Y7gbs39wwM5wF2rNdDojS1OqssL3PEwYkGUFxrHBwL31PHGgrEpu3LzJzniMVDUYePnjl4yiHoPhEGU0Ukl7bhnGCAd83+PVqxM00Itjjm/YeeL7PrP5nOl0ylv338ETAgSo6qvh3bdlIdeGt2Nw18Z/s9JZ/4KuFZ502m7hzMQq6prktcptsOuAwqQjoDHmGznN62QNYB1gaIS0gNWoXLfptu8g8GZ83WexVt8Z7lrvBhg7MDnZPCPWfaPzuTuOFuC+PICCawJShzeOieMYpRSr1QohYHfXnt0URcGzFy+sB53v44UBWVlQytoGpR4eUDUu3ACxMuzu7LcGPwxDvvjiCxaLRWvgbXxRbeOLtAUoITd77Vpr8jwny3McZ33eIAmFPet59uyZTQInBHe+9g1evHjBX/7lX/L48ef4vk8ch5SVdeGVsiLyAx689RY7OztUeUG2StC1Pe86OTlpvf1GI+vN5/v+FuFrFEXEcYjWsh3D8fExq9mcOI6ZzWZUjb4bN25QFAW+7zMej5sYqWGbqdf3fd59913G4zHzpGiY4OuvBMHswc1j4jhCKU2yXOJ4DuOdPcIwoMwLnr98iR8E7TxJi4JKSYaDHXYPD6mWc5arJVpDrGBntE9Z5DawOgh4+uwZi8WSupIYNI5xUUYiKwllDTHIyliGcWW5uNIkg9zBMQ5KSiQ1CEu19OLFc1zt4AqPW2+/w8tXL/jLv/orHn/+CzzXozeMKDK7EpeqJPJ9Htx+wM7OLrIouFwmGKkpi5KTkxfMZ3M832MwHOEYg+8HFFUNjiVWjoKYXs/Ok7pQOC4cH90gnS3o9WKmMxtOEYQBN45vkBc2Jc1wZ8xwMGQ0HhKFNlNvEPh88913Ge+Mma0KHNdQ5jVV76uzklq/bw1pc6EFpEm3vv2w/t0C0WQDGusKXcM8aYChhYmO7rWODQBsPrd6Wp3QqdWC2BoU1uPYKN4e5LpuR23ndgf8rujc6GPrdQPKk/Z5bAFUZ4gblOpc+xKh6lq46iRJ0nLQWe8hS5M0nU65vLSpGNY5k07fnLO3t0ee52it+dnPfobjuEwXc6aLOcYYPvrpz/jss8/47LPPePTokaUr0hLXnhXjOTYIVin7B7deQRljcDz77dx13daNe83eYEndIE1Tnj59yqeffkqeZoR+wHg4IPQDyiIjS1NkVeMYjaol9+7e5cFb94nDiNViiVGaqqg4PzvjyZMnLX1RmtqsvjYrcM7du3e5e/fu/8HemwRZlp33fb9z7vzm96oys6oyq6rnbjQmgQABghChIhUiaW1IGLINUJYle22HraC4cijiMkIb77hxBM3QQrYVXsgLM6xQaEGakZZtkmiYg0iiB1RVV3ejppzePNzxHC/OnV5WgyBRxWa2A6f75n3v3jPe99X3v993voFLly6x2WyMSnFuDt93abWMhaGUBsiMM69PlmX0+3329vbodg1ADYdDrl+/yf7+dTzPxG0rQzOVTsAXvSwXCzbrDVmaIqREaMlmtWR6dsZ4fEYSpSzmc1brDcfHp1y+fInNeg065zt/+mdIaXM2nTKdTxFC88d/8qe89fY7vP3Od7lz+y6z+YxMZUihsaXEkRZplJKlCZYrUVoZS1Opka6FLS1c10FKge1atNo+tmVBniOA+WzBvQ/u8dbbbxOt1ri2S7/TNnQSb1gvV2YtIiNLUm7euMHN55/H9wLm05kJDruOODk55t6777KYz0HAerViuVyx3qyJ4w0H+9e5sX/ApUsj1qs1k+mUxbKgk8Ch1fIK9R/4QYDv+bRaAVma0u/1ubK7R6fbwfcMndy4dpNr+9fxfI/FfEGaKQQSz/Npty8+nVSgEW4z5FoaCas6paRUqeHCmqmbC9tMvlbXleBFQ0V2ji2HzQ8VhFTz2rpW3uJDgK7qophvBSANMDl/jUZfjSU2JcOtsRrjhMX3sNmwfDZNMCpnXlarroTba3jKYj+7rn74kmVGBdHr9ej1eiilCuBaVk6ajmNXRgx6o9lsNsxmM5bLJSqJSWIjYbzy8mv837/7eyalNpDlCY5rIjpYRWK/XINQGlsUUSC0sWLK0VgYSUtKiVDKOBoHxsQ7TVParkmPobXm6OiIf/Ov/3def/11PvX6J7lxcJ2T0yPG4zGr1RKEsRAc9PqkcYbOFRIL27I4PjrirbfeYjGdVoFkj4+PAWi32wwGA2ThkqLJjdl6lrFYGkA7Pjrl5v6BsQL0jMm5X8Swi6KInZ0dlM4qoHVdp0i+qBpqTQu369Nutz8WpsV5lpHEMd1el15vgM4zFsslm8i8FGgJtmUVRjUmLcp6vWE2n7FYLNFJTBSnCAEvv2LoJEkyNAWd+CYAre04IAVZrhBK40iJRCIkaGUSU5Z0IhRgC6IoJmh5CClI04y259HrdkDD48eP+Tf/9l/z+ide5/VXP8GN/QNOz46ZzCYsZ3OUnZMlin67Rxql5FmOJR1sy+Lk5Jg3v/M2y/mMYbePUpqjk2PQmk67zXPPP4+0MdHaMeGclEqYLedGNX10ws2r18lUju+59Po9c+71iOOIy5d3UNqAvmPbOLZNlqWsVgqtjCVgkrn4rken1UZaF59OwvIcngOQ6nOT2Yc1eDWOsqNSTVdgwNYoFRhsMfhmBzzByc0Uzg3UGG+bwW+PEdYI0qjXuF/1fR4ym5Mvx6zvhY0xKyBqzHsLtBvwWj6bsmHZpALGZ1QuBEipHBbzFetVhOOaUEFRFGFZFr7vsl6v2d3dRUqJ7/vcuXMH13WZjGd0Oh3eu/cBw6GJkSdth/V6zWg0Akzq9eVqg9RAYZhkLNkUli1Mag+lqgOMZEWxb2Uu6MKCz4QgCjyPPM+ZjSfcufse6+WyiOzQptfu0AkClsslWZ5USRTj9YYg8JiNJ7x7+4zHRw85Oztj0PY4PT2tVHUAvV4PrTXvv/8+YPJt2bbNw4cPefToEQCXLl3i9PSU09PTIp+UpNPpEAQBtm3T6XToD0zwWdd1jQXl+IwkSRiNRuzs7PDu+w8JgoBOp1PtTV3kopRgPl+xWm1w3DFZmpGkMUJIWi2voJM9pLQIgoA7t2/juh6TifGXevfd9xleGhWpXFxWyzXD0QgUrKOaTrTWiByEFiCVSUEvbZNiQwBKoUWxZyUFeaZMxPFcYFnGMjTPMgLXQ+WK6WTC8bv3WM0XHOwf0Om26bbatByPTadHmiXESQxo0vmGoG3o5O7tM46O7nNyOuZSyzH+YWnMlStXUUrT7fRAa773wftoDa1OG9u2efDwIY8fPQZgdOkSp2eniLMTWt0eli2NWXwrwLEtOu02g2GXdqeD4xg6mUzPiKOE0XDEzuUd7t57YOik2/lYgFQTJEpwOc9Ua0kirLlqWbfxvequ/Ex93gKOsLhajlH0v904bEwo3OqvnmvJ7Gswrf8ru2u2arYrxwrreTaGbMy8+NQAmWr9W6NvA1/zeTSfT3Gx+dzCxohPW8RF8I/50pe+pLMsY7PZsFqtyDITJql0QM2VMe/2faNuyLKM559/nrt375qMvVpVksBLL73Ct7/9bRSG6S5XG4S0sSyTsRcgz3UVUcJuZBotDSlKE18hRCVpCSFI84w8z43PlmWxWq0YDIakqZmf67pcuXKFIPDZbDZkWVKYfVNFpZhOxxwdHRFFEb7v0+/6zOdzgiDg85//HGAkqTzPDVhSglbOe++9x2KxAODnfu7nuHfnHlEU8eKLL5q+ij0oIUzcwFarVYFQEASAMcJYLpcmPqITFIFwPbTW/M2v/O0LbV/8xS99SWepoZP1akmaJgSBb9a3RScB3U6LJMl44YXnuXv7NqvNCksAWqCl4JWXX+bbb7yBwiSaXK3WgIXleAhhgzCR9LXWoMERuckADdXvoiWgQAiJJSVKaYQlyfOMLM8IfB/LtlkuV/T7Q9I8Y7WY43keV65ewfM8kiQmSxN83wNhcjw5jsN4MuHx48ck0QYvCBi0PebzBe12i8997rOApt3ukGa5UUEKQb/XR+mM9957j+XCuGT87M8aOomjiJdeeBEv8OgP+rRbHaQUOJ5JLuq1fTpBh3YrQGtIkpjlcmXoxPYJWi0810Nz8emEMNQ1Aw0b0sF51hluY0bzaDDrbRYfbjPgJvCVd8IGmDVAkeYcnmgTbgtXjX6fEO/Ozax5ZQtgm4sKaYBnvYbqXA5VLT3c6ibcWkO1lC1pbhuEIQzDZ0InFwKknnvuOd3tdhFCMJvNSNOUIChCDSUJOzs7TGdjPvOZz3B8fMxg0KfdbnNycmIcdU/PKuZtuy5SWoxGJuKE0oL7Dx7hOMZ6EGATJ1WKdZHnSCkrSaJU/UEpccFms6kklNVqheeZpIZxHBPHSeUMK6XxJ4ljk4a+1Tb7Q45j/K/SNCXPc9rtoMqk6zgmWsVgMKDTqfX9g8GA/f2rgNmrStPU5IxqG7D53Oc+R789YLFY8OqrrzKdTo3Pk2uhlGI6nZJlGXt7eyYqRvFs4zim3W7T7/ex3FYVFDdNUz7z6R+/0Mznueee0912ByELOskygpYJU5QmMTs7u4ZOPv0Zjk4MnXRabU5OjlksFkzHY5YFnUjXw5IWl0aX0ZhszfcfPMJ2PRzbRmU5cZqRFftLEo0QEsexyDMNaLTSCGmyPKN1TSeOzWq9wvdMksM4TQyd2I5RyQmJ57skSWxSb7QDVJ7huA5ZHpMlCWmc0+m2zR6XJXHsDKVyBr0hrb6PUAI09Id9DvavoTFAW8Y0bLWMocznfuxz9FtD5qsFr7z0CvP5zFhFeja5yplNpqRZzt6VPXZ3ds2zLRzLjdFNH9sNoEEnn/3MFy80nYRhqM8hhTlRSwrlhfPSQcmIy6bNFo2W29DVAJ7temFDSCs/1z038KOqX18/Xz88119jbg1A2QKjYtzy2jY0lR09OebWvbJdo6/6KHprtGusDird1dMV+1l08rQlzzXjcWkMYNKql3tRlmUZ9d3wMpPJBMuyePjwEYvFooiwAC0/IB6bVOuDkVGDvf5Jw+BPT8d02m02UUQUFeazGuwipEwpR5Vvx81SSlClFJJlWeWntFqtAGMFVddXJlV8kWOqzI5amneXpuZ5rlFFNO48T2m32/i+z7Vr1wC4ceMGUkpu334HMHsfu3s7555ZSpxsePGl53n0+EHxdn6tynd1cnLC5cuXuVbEFkzijN2dK8RxTBRFaCWYzRbV/tvHYk9Kac4mU4LAo9VpE8cJaZJiWaqgkw3DwWUmkzHSljx88IjlYkGSJkgpCDyfOJ2glWZ3dInxySlXXv80Cjg9G9P1WqySiHWamuSIWuBYEils8syY+Gep+S1LSRsM7Vi2oN32QQiUznEdDxBsNsZ52KScEZCBVopovUFLYwKe5wlaQxxFIAWWa2ELmzxTRupHg5fhem1c3+fq/jVELrhx4zmEhLvffadIcijZ29s1mgDTiizPiJINL774PEcnD3Bsn/3rV3Bsx7hLnJxy+dJl9q9exfN84jhjb/cKcZywiSKUgtl8jrQspLA+FnQCJX8NzefyQvWFLabarBeWVSumHtYAVne2PU4D3MqaWxJUcWtLtbbVVzGTEiCridSA08CM7XUWf6rRz6+Fei31EPVc637Odcw5YNsCzXBLiipXUI1TPofzk/0hy4UAqdI82kRdTmi1/CIcUUan0yaKIvr9Pp7n8f4H9zg6OsJxHHzfSCqr1RrbNmCxXC7pdDqVc2qpmrMtywQyK0qSJMRx9KF7Mc0sv01J88Oo86PWAAAgAElEQVReC5r+RVVdUQDeE0KqLHqpb7T9gOVszsHVa6SR8eOK1xv6/T6i6G+zWnJt75Ps7e1VUTOmZ2Ne+eKrWJZV+UlNJpPKUu/69eu0221sywUtq2c8mUwKw44VncElZBn/zb4QpPDnFt/zwYM4ilEqptUKSIQkVzntToskiun3BviOy3sP73F89NjQiWfoZL1aY1k2whKsFkta7Y6JwiAEHTdg4lhYmbHYExh1X5wkpCou0qookxHXEmihIdMITIBZrYzUjlaFdGUkLS2MlKWzDJ2BNMEhzL4YGixQudkDNUEFNbmykAJj6i4M5ARewGK55Oa1fbJljNCCeLmkNxqYpkqzSZZcKehkE2/QSjM5G/PSF1818QBThZCGTnzPqHqvX79OO2hh2x5aS4LAN+3WU87OzvBbLXr9EVJYWJaNbV/8vUtogsO2JFAzXfMnpGamNTg1GXTJkMMtplu0aGBfk82Hxf8hNMesmHf1pyGhhJQo0pxfCUCU4zeQKjzfd3MujarVvIo+yvWUz6n5TJ4EwrrNeYAKq2mGW0cJXs+qXIjXovligeO6OK5LmmVoJK7vFUeAF/jMFnM2ccTVawfcuHkTaVmsNxukZZGpnE6vS6fXxfUC+oNRFbS23+9XvkB5lpBnCbYsUiVIq3ojboYF0h+CRt9Pbs1VWh1KZyidVSrDUhKr+q2uFwokbVQopbpxd3eX3d1dbNtmvpgynU6YTic4jsPu7i77B1d5/ZOv8fonX+OTn/oEDx7c5+HDB3iey87OZaSURZ4qm9FohOv4rNdrptMpy+WS2WzG2dmE+XzJZmPUj+XLwMfBmXe+WJhAva5DmmUoZUIDuZ6L5wU4vs90OWOdRly9ep0bN59HWjarzQZhWWRa0e13aXc7uF6LwWjEZDphPD1jMOgihSJTKUkck0YJtjAWb7awAIG0JJZjoTKNVqAtQf0OoxFCIy1DK0IIc0YjpCQnRZGgVA7CJDxUxd6oKsCPoh1aGSArFI3GsjRDCrAsyc6VHfau7uJ6LovplMl4wmw2xZYOOzu7HBxc5fXXXuWTn3yNT33qNe4/+h4PHz/A8112Ll3CEkYlbTsOw8EI1wtYb9ZMpxNWyxWL5YLTszHzhdm7TNOs0G6kKH3x80kBjbf6kmmHFQPdYt7Vn3CL6daMvCEpFPeqbyGNa42hK7YfbvVX3Q3rserPNQg2ZrLF6yuQqto1F1ID55ZEVq2Oak3V5+o5NcZsVK6eQQmc9YK31xk2nlLYPD+bYj+znp6ixHFc6fPBpDtvt9sEQUAURbRabWazCUmScOPGDQ4OblT7Lp7n0WkZKzaAIAhI05TxeAoYFVu3267M3IEiu67E9WziXFUSkCxYhdYaLcxLrNb6nOxjgtaWRTfuNAHpB8XCK8dcr2MODg4AKnXfZDLhzp07vPjiiwBcujRkOhuDUFy7ZiwAWy2ft968zWg0Ym9vj+FwyHQ6pam1DIKgckTOcxPh3fM8BoOBCcFku1Uw2o9D7L44KuikHQCC9WZDu9MmaLWI4oggaDOfT8jTlIODAw72r6NyxWQ6NkF9211a7TZCG3+hNE2YTOaoXGEd2HSCLlGqiOMILYrsupaF51lEaRGdRINlG3DSuUZLjTH5w4RVFKCL+IrlG6AojDAkoKRCalkI1AqthPH5ksL0V77YKFCy2AvTsFnHHFw/QFuC/Ws3QCsm4zF3373LSy++iNZweWfIbD7m/kPN1StXAE275fPOO3cY9IbsvfQyw+GQ2WyKzo3RuhAC3/NJs8zs7WVpQSc+g/6A3b09hOUipEBKgRAX4r32zy9hcQqbEsOTjDhkm/E3OwibbRpAUHP+kkXXjLkcO9yqVx5hJY1t9d2YL+V8KqBptG2sJzzX14eud6vT5vUaEJsSUhhWFetJF+sOG9fLtVUgVD6/BnhuLegZlAsBUl/4whd58803yfOc0WjEbDYjiiJct1eAVIsgaBNFa27fvk2r1aLV6tBud41JeBDgFuq+drvNeDxmb28PgNPTU3Z2dox0MjcWT5PJBInJsdRkzgq9BUAfVs7fL9t/v/P5opUwqp3ivrRNKKNut8tstqjmZ1lWBbyLxYIoXuN5TrUnsLu7w2c/+2mEELTbbRaLBev1muFwWMXiK5NJep7HdDpnuVxWUlav1yMX1pbf1EUvn//Cj/PWW2+S5orLoyHT2YxovcFzemw2Ea12i8A3UsGdd+/QClr47TbX2x2yLMULAjzHNfEOuy3Ozibs7O0igOPjE3Yu7yA9m8V8iVaa8XSC0BrLdk1oejQojRKFtFTwayFNanaTV8qcyUy9UmVnGHxBEzlIIdCYFyMhDeApBdIynWsFspDWVA6uKwurxQ7zyRwNTCZTLNum1W4hECzmc6J4he/aCK6CLdnZ2+FTn/o0Qgo6nRbL1ZL1esNgMDB0kmTESWxSivg+4/GM5XyB7XlcGl2i3+uTabPDpbQmSz4GESdoMN8muw63gakCmKJCLQVQSSqVxEKFF+cki/JTfaWaRdnv1vhhxfTDRvttXKj/q1GvMXbYBLjtQcLmOFtjNNbbnFvYuNf4Xs96a0X12htzqM6N8RpP/anLhQCpr33ta8zncz744AOkNBEe1ut1lU4dKIwoHITQlWFFt9tlNBptMdk4TRCWzY2D6wC88cYbZn9HCFp+YfRgO1uGEk8AFbWPVKm2AbZ8raq2lYq+eAMudIVbWCZEoeKjeNPWdZ8Ijh8fcfP6DR49MLH62p2An7n107z44gsATKZnSGnM7I+PjP+LbUleffU15vM5SilWqwXtdpudnR2EMKk74igpfM1aVap5KW3iOOXsbEKr16t8vz4OIPW1//BrLP7HOe+//z2EdGi3Wqw2hk7K552rkk4MLaRZSrfVYzi6RK6UUbdJiLMEYTncPLgOUvPG77/BqGWcvtu+a5JR+h55mpktRq2NL5Q0ApMW9dZjYamAkNL4VGnQjjFd10qjcpCu6cPEAgQhzL6T1sbajwLQtDb7W4hCQCv2tbQSnBwZOnn44KExGOq2+Jm/dYsXXnwegWA8PkXaGmkJzs6O0JjIGa+99gkW0xkKxXK1pBW02d3dqYA32qTYjsLGNZHdtUbmmihOOTsdE/R6qDwjTfNKSrzQJWyeaoZMk3WGVKBQc92GhACNz+FWX03GX90rgK/ZR1j0EW5dq/vcmmUJik1gqvpszLWaV2NdZb1qDY1mYWPMc8+Fxng1SFEDEef7oVE33OrPzKWcV3MtT18uhOy+f+M6X/3pWzz34gss1iuUANtzibMUpE2a56yjCGFZ7F69ws6VPbAky82aOEtZrVZMp3Om0znrdVSlnc+yjOFwyNnZGfP5vDAZj4s074VU0pBq/jJF6OI4v6cl1NaeVLM0r5XnLMsYj8fMZrNCQmzx/PPP8+qrr1Z7XLu7u9y8eZOdnZ3qaLfbPHr0iCiKCIKA4XDIcDisgtc6jsNgMKgA3Ji073P16tXqLdoqArQGQVBJbRe57F+/wVd/+hbPv/g8y/USJTSW7RDnGdKySdKcJEuwHYu9q1fY3dlFSotVtCFOM9brgk4mc5YLk7QwS1OyLGUwGnE8P2O+mLOOYzZZimNLHNdBWnW0CWOlUHwGytcRISRobbLolvYzEiiMKrQSoAu1qgB0Vr0oCVHSCwaQhDb/Mo3ghhCQZSmT6ZjZfEqn3cZvBTz//HO8+sorKJ2R64ydvT2eu3mDndEOO6PL7F7aodNu8/DhA6MO9QOGgwHD0cA4GY8nSGnT6/VN4k+V0u/1OLi+z5Wr1wyd2A7SsbBdh6AVEHwMwmdVqqdzTLhSX4V1tepuU0IIm32VGFb3EzavnweAAiS2VGWNClt9bw0TfuhnM58GyFUgVH8vxymBogKacqKNgcOyj+a8wvo5bKNTWPVXgW3zWTSqN8Gx2c2zKBfCT+qf/4v/WQ+HQ37rt36L3/zN38R1bQaDgXHUtSyE0Cb1tm1jO5JWq4XvG6OA8XiMa7mV1HVwcICUkjQ2AVOvXbvGH/3RHxFvogoY8iIlB0Aq7cJhswYPUSR7q0ComGdT1VdaAGZO/gTwaFWDXqme01psgVYJao7eYNs2165d4+d//mcBeP6FmziOhcaYzO/u7jIeHxO0/Gr/6p133iJNLa5evcrLL79MmqYsFivOzs5YrdZ89rOfRWDxwQcfYNsmg7ABJZPJN01TFpsNVhFGyLZtDvafu9AbU//8X/xPetAf8lu//dsFnViMRgMWi4JOJHiug+1YWLak5bcr9d9kPMa2XDabNQjBwcE+lpQkcYoWmv0r+/zhv/9DkqUJTEwRJT/NM2wEsbSMu4LShXm3iUqhMRKWkIZOtAaZN0RurSCH3MlM6EdDVMWWlAGsPDNSsu3bqBxUlqMLcBKWhFzjYCxYr167xt/9uZ8DDJ1YjgBLITTs7O4yOTui5fns37wOQvPd775FnFhcvXKVl156mTzPWCyWnJ6esd6s+exnPgva4nv3P8CyXXZ3drGlje16BL7Zq5quVniOhZTGwu/6wfMXmk7CEB0WH4orNQMtrtfgVdw/JxnUaNWEuXCLWYdlp1uSQ7gFHEXFH8i1mzOpIKPZ7EPa14BVrnV7zucRMWweZeeNBxM2l92cQ1iOt72Uavx6Qucx8f8/flKLYIfR5X1e+Ymf4drb9zh99IBlnNNu94hXC3Su8F1Jso6IcthpX+Ly4Apn2ZhZusT1HNyu2ZM6fnzEa6+9VgHCZrPhlVde4fj4mDt37gAmzJAlC9CIE6PGaRSti7flEnSKZ60oJSerAhndiJ4uzp3B7DWw1Us1iDlbOZlOOZ08ZB2ZdCMnZzDsdQl8k5n39PFdPNuhLQPyhYn2fqXXZ54q4mjJ9z54j8FggCUVrcBhNNxDkLNeL9jf3yGJMxxbY1maNFkgiIsUJAbwpWWRFFHjL3JZBLsMd6/x6pd/hv137nHy8D6LSNHqFHSSKjzfI10npDnsXN/h8ugKZ6dnTLMFjuvgdnsIBCePj3jttU+giheUTRTx2kuvcHRyzJ27d0GZ1BV28RKjkwRLWrUaWBs6EWhjml4YnxhM0gWdFE7iNqi0VAfrLRcHAUjLRKtQsSpShIB0LFSao+LcAKCjyIk4mzxiFZ2CEBxNBKNujyBwQeWcPJjjuw6B0yJbzgDBXqvPzFMk8Yr7999n0O8jUbQCl0ujLgLFcjVn/9oecZzi2Brb0qTxnEhFRPGGZCNxOwHCtkjS+K/lt//LlZBKoqDBdJugRZPvbzP1sMmcy7Y0voeN7kqQqsZo4lLYAL5wS5XWBIKtuZ4Hmkat8lYNmFXPdb2w2V89twrDmmtv3CjnuiUtfZ85VrMq61drPfcs2GrwQ5cLoe5rtVrcf2T2Y37pl36JTq9LpnJmsxmWZeH6DqJ429fkPH78mHdv32G+mDEcDbb2U/r9PnEcVw6qSilc12U4HPLlL3+ZL3/5y3iex2QywXUNCHyYNNlU4324GfmT6rwfppRRKCj2wbTWbNYRJycnPH78mMePH7NYLKr0IVEUEUURw+Gw+rxeL6tsu1prEy3+6Mik/l4sqn2rUhU4Ho8rw4p6/ItfglaLB48fAZpvfuObdAc90jxnNp1jSQvXcxCUQYNLOrnNYj5jOBiY/SiMgq7fHxDFMVaRckXlxgF3OBjy5Z/4Cb7ylS/j+x7j6RTHcRECVBF9QoABG0p3AmFedIqwSSaSkkZIyLW5aGiodLMteyk+FzSmlKKsliUm1JFliypobZ7nxf6mAgGb1ZqT0yOOHj3k6OiI1XqJQrOJN8RRRLSJGI6GJHHMJorYrI1xTZZngGa92vD40WOkJVgs5izms4JOMs6mUybTCUmSkmbmyFE/jGb8Iy8hNQwZZl4DRM1za+ZettoGtQqaKuYfUrctv5R8vhqjUTdke5xq+CZQhY2+i7nWKzj3OSzv120r5NqGn635lQOX82ygXb2mrXnXKy/bN59V/Tl8Yn1b4z6jciFAKlU562jDzt4uXuDzH3/jmwjLqfJGpUWiNstx6PV6CKmZTM5YzqZVxIbyWC6X3L17l4cPH/Lw4UNarRYPHz4kSZIqFUYZNbyMGgFPhkNqmmb/eUD1g8p5sDt/WJaFFCbCe2mJV0alqPJYeV7l73V2dsbZ2RnHx8dVbEOtNVG0RgjBcDhkZ2eHXq9TRb3I8qQynBiPx0wmE+MEvViwmM1YzGasl8tn/8M+45LlOcvFmsu7u/jtgP/oP/kG0rbZxBFJnpKpnCRJsW2HbrcLQjOdjpnPZ+QqpdNpVXSyWC54t6CTBw8e0moHPHz0kCRLWK1XLBdLPN+n1+2xWq+No66o4zuarShppCXbMvtWwjj6WtKYkKdJAYpKI4VGGJMLsxhReEIVZCUtiSWtwhxdVtaAFS0KicDCdm28IMB1XDzPJc+MI3euDJ30un1Ac3Y25mx8xsnJMe1Ox9BJrokjoxYf9Adc3t2h1+9gScv4hmUJlpBkmWJydsbZZEqapqyXhk6WkxmrxcWnk/JNvnzDr4CKbUCpPtOUBKiAoOilZslVB7B1sUafop9zfWyBiOl1Cx3K8Wn00fjehIUat0JqqaWx9sZat+dcAFyj27A5lw9bd1g+u3I69Zj1cwyrNZbNy/vPqlwIkNKuDa7L/eMjMsdGOxb/1S//Ml//5jeRvk8uJPP1kslszOnkmMn8jKBlsbPbx3O0yRdV7DPt7u4yGo2q7/fv36fX6zGfz6v9lzKoapmioglCTaA6D1IfBlQ/CIR+0IF0SZUm2iRobDQ2ruvj+gHD4SWGw0ukmeLh42Pm8zlZnpPlOb7XwncdWr6HJWA2mXBy9IjlfEoSrYnXGx588D5C5bT9gCyJmE/HoDIGvQ6jQY/d3RGdjo+UCqUuftJD5VoI3+XByRGptNC2xX/5y7/M17/5DaQfkCFZrJdMpmPGkxNmszM8z2Jvt0/b18ymM/JcVXQyHI3ItCLTivv379Pt95hP5tjCxrJs/CDA811aHUMnsqIBbSz0pIkgIYVEagzIFKblQmIiUWhNpjQaCdgNU3TjdyQLQwrT5hwoiVrqEpZHpjSbRQzaQgsLx/bwghbD0SWGg0ukac6jo2OmkzlpmpOrHNdrmXBjnsk7NptOOD1+zHIxJdmsSDYRD7/3AULndIKAtKATQc6w22I06LNzeUi75SOkQumLTycVmw9L8Gkw5OZbflj8CRttw8b1RrWSYVd1aIIPW4BxrmUDBBtjVH3W4NDs58MYfjWD5nwbc9oC3eJz2bgJUNVcwwbmNTqp7pVtKjAKS5xqdFXf2+6LZ1bsZ9fVj8qPyl9tEYWBgsqM6b8QoogaVER+MAKMMeVWxkJOFpGosqzYayyM60oRRqhC8VbEKhJSbLkIGMGnsNg7t5ekEUX4IkGW5li2RChjAq6UpjS5EcUfIYwEJqoLmHqi6pB6WF1EWKeyLFSFGpFib4tyTiUwlntewqgaKS0Qc42wZQWguhi/gNsi8n/tIlFKedUzamyiiR+sPPhrL0+83dccfIs519JD2Ghb/mlCTBMozkkZ5dWiw/pas/42SFWQGZ47V12FFRg061bgutX/k23Dss8mwm0twlyv51XWCbfnUo0QVleqFTbmdl4CDetLz6RcCEkKaaGAVq/L0fEpo909NlnCwXM3+dJXvoJwbJQUSNtCC8w+TLwhzRI20Zo4jrl79y53795lsVgQxzF7e3vs7e2xXC5ZLBb0er2tQKrld8uynoi4cH7f6cPUe08rQZWHYX4SLLsKU7RamSy8mygqAuNGlal4KQ2OpxMWiwVaa/r9PtevH7C/b8yGHccBobAdWbQRLBYzzs5OWCwWzGYzHj9+jGNLBAqBQn4cuI+0yDS0+z2Ojs8Y7eyyzhL2n7vJT3zlK8bYQAps10bYgs16wypekyQJ6/WaJI15984d7t69w3K+IIkj9nYNnSxWRsXX7XaN6s4yQfb6/T6WNNHPSzqpjEGVQmXGGRpNEThYoxrGMmgjLQlMivltJ2+DTpZtoVUJMqWqWSAtgbBkbdKORNiO2UvMUparNdPZlM1mwybasNlEOHaDTmyb8XTKcmX2NHu9PtcP9jm4dpXBcIjjuWgUtm3RCgIsS7KYzxifnTCfLZjNphwdPca2LYRQSKG2AOtCl7ABQGHNZs0RmiqlBFACTlgz123pIGzw47JuuCVtVCq2sDGFsHE0mXy4XY/y2hPtw6rfGrDCrWoVGJR1qzEbcyznX6333PjNtZZgGIZb7eprxejNLstnV14Pt7t/2mI/w75+6DJfLrh2fZ+7t79LbzRkvlrTGQx4/7vv8De+8Hlmsyn37rxNttnQ8l20NvsC89Wc5WzOzRc+QVRYp5Vp0UtAuXHjBvfu3aPdbnN6egqYoLOj0Yjvfve7FWhVOYIa5ujV98YZaoD6i5QfVM+2jYOxVia7K5hQTipXVYgjzzd7Unmesy72BDabmPawz2JhIqobs3yzP2U2v5VJhuhIVqsVcZQWfmIbFosZ6/WSfn8IKscS4BRGJBe5zJcLrt3Y5+4779AdDpit1nRHQ95/5x3+xhd+jNlsyrt33ibbrAlaLbTKQcNiNWMxXXDzpdeJNhtAm6SUWYYqmO7NGzd49+67XOte4+z0FAF4vs9oNGA2m5lAslZhPCGECR5bWEqYMFqF8y6FtERDpVskolJgrEpVaSFopCOVKyzbVJOuRGd1ChCljOWgJTwjNSlBFJvo+rYsI4YYoSfwTK4opXLWc7PfutlEtId9Y+SRp7TabdAtlNqgdU6uFN2eoZPlcmnoJIpJ0ojFcsZyvaQ3GKJVjiUEnn/x6QQaTD0sv4flnUpyaLBcavjarlcx4UZ3JVBVoBE2pIiQCplqdVlzYmzV22rXrFyiTTn3rftNUN2uv7WCRv/N0phiPVwDeBrLalRutK/GPPcswurTk+t+inIh/KT+u3/zO0afkitUlqLyDJ0mkKWIPGU1m9IJXH733/07vvMnf4jOMyytaPsB0hIsZ1FlqRcEwVZg1263W2WjLSOB37t3r4oTOB5Pq/QVVY4pzD/+LMuK6ODngaYpgP75lnEf5hu1FeGiCEgbBA6f+9wnAbiyO2I4HHBpYBwnsyzlYP8qjmVXqUJ830cLA0RpmuI4Dg8fPsR1/SoaR54ZSz8pJYPBgG63V0kDjuNUBhRSSqIo4ud/4T+/0LZbTTrJ0wSVZ5Cl6CxFFnTS9l1+9//6Pws6ybGUou37SEuymhuJFCAIWhiFnVHvdXtd0iRlOBri+4Y27r3boJOzCUmaslqt0bowC9cOSuXkuYl7hy6kH1EHERaWNJl8dV68++gqnBIKEwpJ6iIOkmmjckyEC0ua9B4aMpWB0LQ9i8/+2KcBuLIzYjTsMxq2AY3KUvb3r+FYFr5nopl7gdlL6nS7pGmGbTs8fPQAz/VJk8yEx8o16/XK0El/QLfbN+tA4tgOk8mYTOUIKYmiDf/BL/wXF5pOwjDUjc/VOYQGAy//wHnuvMX8G5JHzcibIMU2M24w7QoEngCieqwP4+QVMD5R9fx4xThNaAprANpeXglYZc1w69iqX809bAwdNp4R9edqsOZ8qkGfCZ1cDHXfj8qPyl+olH5r9UtI6XRdW9xt+62V+z+ikHiqfaLSVpwyrh61Gk9otDSWeEIU/rdmAwnLqja1KG3OVeH8bczFSwMIM5c8zYskiVTR9aXCvNtIqjBLWS7IM1H7SdlFEFpVWAdCFSKpNl7XNKJtmYBFShdpQ7QBvVI9Wa5clNKfeVkqXAKrsF26eobFd6lrLnEuqv/FLWHxN9wGqfBDmC1N/l/XqVhtdTPcUr1V9Wm2K68UR3Oc6nNYX/s+gBV+v9vNfqp1NIcrxwobAFP8F25VreZeAlfZ31YfVVdhNdz3k/yqdsXNre9PWexn1tPTlHLPR2D+YSmJtuxK7TbYu8Lk8SP+1t/+Oyil+Nbv/T9c2blMFG2QyuRDaqrlbNuu8kQpZRLiLRaLSiXYarXI87ySnspU7edTa3wUCd6ksNFo8lywWhrz4GhgIk1I2/w8Nib5I1JUasksy2h3fdLUpIMPgjae5+H7HpPJmrOzM2zbxvd9Bv0RQRAQx1EVRsn3hwSBX+13penFt9oyKjYMkxUgpIWyzDMSAvp7V5gcNejkd3+Xq7uXiDYbLG1hO+ZZa6XRShk6cS20EmSpwnIsFvOFiYJuQbvVIleKaBORK0MnWueFXk5XHk9mX7PcXyyMLIQBiDLyVp6b1PK1QUYRtUIKyA3wSVmQvBRmj1CLyphDCBuhFGli6EQrTdTPEBg3BgQ4wtCMsGThcyXIs5RWxydLYpaLJX7bpIH3PZ/JdMLZ2ZmJ79gKGPRGtIOgykrQCgKGwYBWELCJI2zLJvsY0EnFdykZbFiBU8Wwy0rUjLhozRZ/bXD3qkXzfpMdh81zydibQBZunWt03BpwC1yrHsq25VzCRt1iniFh1W1zSlXLc8+gnF9jlCen0wS4stHWPGow3Crhue9PUS6EJJUnMTpLEcoEQLUcF8v1kV4b4fdIcHF6Oyxzmy//zM/zT/7pP+PTX/wpJpFmnohKNZdlmUkTPp2yWCwqR9ZWywRYLX2MZrMZJycnVeqOpgEFUIHVR5KFVNi4Xgu05P6Dx9x/8JjpZMlqk7BeR6zXEYvVmsdHJ9y7d4/bd+9w++4d3nvvPf7sT/897927y2o5ZzEfs1kvOT05Iok3qCyl0wrI04TTk0ecnhyRZwmB7yKFZrWcMz094t7tt1nPJ+xdGvzVr/UpS57EkKcIpZCWg+W42F6AcFvgd2s6yWy+/NM/z6/803/Gp3/8p5jEME+MY22WZWR5xnyxYDKbspgtWC7mLBaLyuDg7OSMydEZ0/mc4+Nj4iQ2gGJLpLQRRebCUhqRZQr5UoqTorpv8kUVkdGLvalM6SKrR2l7LrZi/pm9L9NPgVOAheO2ENLiwYNHfO/REbY1lkQAACAASURBVNPpkuU6Zr2JWa1iFss1j48Nndy5e5fbd+7w3r33efNP/sTQyXrGYlbQyekRabRGZSnddos8jjk7ecjx0WOyNKYVGJP15XLJ9Pgx777zNpvFx4NOoH6/ryWCcEtCqiqFDeCpGHBY3a8ul3fCcyAS1n1XAFDXPodqYeOgARo05hbW1Rs9VfWboFHWCGvQaYJjcw4lwDTnXfUdsn2vOYeyLY0+yr6LcUuprRq7mv+zKfaz7e6HKyrPkMJC2Fbxj95GSl1FgZ6vNuztXSWLNnzw3ntcv3qFr976OyyWG7797TdYr01wVgDXdavI3sBWGvdSuioD0Jr9icLhsuAQRqL68CgUfyVrL6y6hGWRZUZKMlEk1iQ9sx+SZ1kVHaLMoBu0W/S6w2qPablcstlsSJKskJQsJtMz40vjeSiVsVwu0VpXWY3Hk1MePPwenu8wGPY+kvU+TVF5htC2AQtLghJIqcmFA7lmvpxwZe8q2WbDB++/x8GVgk5WEW+88S2i9ZyWH4AAz3NRWhspOtfYLcekodAa27HQGhwpyR0H23aqyBKlZXae5VX+p1JXWEW6ryTywohCClSusWxJro3EpDJVRZrQgOVITOAPA1bGGrCQGIE8B9cVICySzEhJURSxWa+JkxYITZ6lpElMkqYmEzUQdAK6nSHSMr5Xq+WKaLMhSjKCwCcILCbjUxzPx3VcNDmr1RKdaxzPxfN8TmenPHz8PXzfpj/o/7X89j9cqQGlkhDC+nrJjAusqACpZLxNQKhaVe3La1RMuubxdb91p3XndZ8lMIQVwNXXqt4rAKmvVgtpANi5+TeX25hGrQY810+4DUJhs89y7bAlmdbzpH6O1XKaoz9duRAgpXOFNhnhEJaRYFSRlptcYbkBSjhMF2OuXD0gURmB5fKNv/8PsdwW9771O8znJqad4zg4jlNFk7Btm5OTky2LPN/3sW1j8m0A0UhSTbXfR7Z2LQqgsky4G2C5XjOfL+l3jaGHVhntwEfpWrrrdDp4nmA2m7HZRKxWq8I4ostwOMSyHMZjkyixVP1YlgHn2WzC0VHCanHGcjlnNpswmZx9ZGv+YYvOFYoMqRxQGimNU6sUIKTG9looYTNdbNjb2yfRGYFd0knAvW/9DrPZHCHB9Vxsy2EVrdBa4+YOx6cniPI/Ab5von9kaYpl2WilUFKipCQXeZ20oghVvuUQfo6ERKGqFYV/lGVLk1ZEFOFqNVgScmV8t1Sh0qTqSpDn5gVLaxPjb7XZMF+s6HX8QpAzjttKCaRjXl467Q6+J5nNDZ0sVysGvT6Xuh2GwyG27TI+OTOD6lp9memU6WxMHKUsF6csFnOmswnT6elH9Gs/XWky1fLKE9JCWSvcBik+rG3dpGLgNeCEjevhOVAq+6mPCkTKes0hwifr/Xm8/glQKocvLjalvGqeW0DZBKd6sOa6yt5r8CrrF3NtgGg15XNjPE25EOq+H5Uflb94EYWkUavTmlv5pVOvMeGmAov6vaPI19QwstDl/VwXUhAVcNTOrXUf5x17EaU5hq4NEgqgEpYo1McUUhUmbTxmjkYSq/2qpC0KH9wiCWIVXV3XRqblPMr5ytrnTlRhl0TVpsweXflblc+mrGOJIvJ/sRZRW6BWy9SVb+/HpITnBJiw5recf+OnARLb7Zs36/ZhBVBhWS+sG28x53ODlj1VcyqBjLBm7GEDJKAC0m3UOg+QDWBtVmvOtzFGve7tOTTn/0SdrTnWa/iQoZ5puRAm6L/6L/+Vti0X2/OwHQ/LcdHCMVZKCNI4YWd0iUcP7yPynEGvS7xZ0W75jAZDXh8IfvEXfxGA1WpFt9ut1H2tVos0TRmNRpX58e3btxkMBkW6j6gKvpqmqfGxUnXsvjL463Z5diboGq+4llO6KrXaNnu7l3j+pkkVb1uC/au7RFGEVQy9v79PEs9JkoR+36hgrl69xmq1QinFZDLB91uVUclmbQws0jSljBnY8R1s28ZxHBaLBf/1f/vfX2g29Kv/8l9pKRzclo/EwWn5KG2jlUBLQbKJ2b182dBJmjHo94jWS9qtgEvDIZ8Ywtd+4RfRGtbrBp0IQSsISFOTf8xxXQTw3YJOAt9nuTL+VVmakWUpWZaTZwZELCnJlap0cxVASatCTaVzLEfWCFc4/0pLVkYU1Z5UESpJWtKoBREgnYIuFY5jwKfTcdjdGfH8zasAWJbg4OoOURRj2WaMg/0DknhGHCf0+2Y/qaQTrRXTyQTPDbAcG4D1KmK5XJFmiQn3JCXdwMGyHVzHYb5Y8N9ccDoJw1CXjLxxjSfAwdw4x4SbDP88ww2royl9hI27zf5qoKkBs0bOGlDCirOHdWfbK9pey7nzh62vniNb62vMqJxEca1uf34S9TM4//zKZ7ANftVIYfhM6MQK6xn8tZX/44+/EyJNagMpQKKx0MbJVGhsAdFmiSWMF71C4gZtbK9DLhwWqk1v93k+8YVb/Nnt75HhQq7QOEgtWK8WgKLf72FZknWWkgpNlKdYjoUSmkyblBk5OUqaIxcZSuRooatQOgILoSVCm7NJzyq+71EDUvP3Kh09JcgUKXOEUKQqJzM5xAEbIW2W68QAt1Y4tsWg08F3XHQWE6cZcZziuh6u63NycgoILg1GDPtDOu0268WSZBPxiVdfodtuEy0W3Nzf59JgwMsvv4hXMORLoxGvffZv/upH9JP/UOW3//g7IUX0BksKhFZYWmNLsFC4tmS9XGIJibRsFAI36GC7bXLpsMwNnbz+4z/Nn92+T6odRKZQ2kEgWC8XCKHo9bpYUtR0kqa4rg1Sk2lFTkamDY1oS5GRkVNmrTWgJKUNuaEXlMByjQQlFCht7AJlkRBRaAN0RgqTtaSkTFxAy7LQpFi20QUmeY5Co6WJByikxXKd4LgetlY4tmTQ6hI4HjqNiPKMODEBaD3H4+T0DLRg1B8yGAzpdjqs50vSOOa1V16h32mzni+4ebDP5cGQl19+EddxkWgufwzo5NbhYXjILUJuUZ45hPDwEMKQw4KhHh6GhLfg1q1blAz3sPgPbnF4GAKHhOEtDm9BeOuQW4eH3AohDA+5FYYc3rpl2h8ecgs4PDyE6mw+h+Etw8Rv3TLtD0NuhbcqgAoJOSzmd2hmwmGImdshHHJYzNesr+7ZlJCi7WGJkYdFu1twWMDGLTB/bnErPDRzKe+Hh3ArJDw0A4eHcHgr5FZ4CLfg1q2QW4fmudy6dYvDQ3MOw0MOw1uEh7cID8Oq78NbxXOBZ0In9rPo5GmLcaKV2LbZcLakUyQ7rHKzG3DAOEQ2E/VZlgmp9LW/93UAfvKLn+cf/f1vsD8y0kWymfHCCy+w2Wx48803Abj23A3G4ylaigJwciSiEUC0HLPwNym/b6l4FGYj7QeXSv3DkxEomkFqdWFeHscpi8WC3R3z5pskCZuNBJ3jOuaZtD0bpIUlHRNFXRpjCN/3cRzHROSYG6u1vb29InW8ceqdTqdcuXKleMaiigp/0YvKcoQtzTOzBLbtYlk2QkgjieRgS4ecHEsKpGVh2yarrBQCheZrX/86CMFP/vjn+Uf/6TfYHxXPeD3jxRdeYBNteOutNwHBtZvXGU9mBnryUp1WSNmWcbxFGL8nhSosIsp4goU9hVAIyzJGH1BFqSiNMMokiLkyjbTSjQSKhVuGKE0FReX7pZQiSTKWywVKmzWkScw6kmid47oWIOhoCywbS4DEQloOvqcIfA/XcVFaMV/MWSwX7O3sGRcHIRmOhswmU/auXMGyLYQUeL5Hr3vx6aR8o9+WEhr3SgkmPCc11fJEU2jaVmnVXVVSR9gYJ9w6l/Ua7RtyTHWlmgvb9ZoDNiSl7fGhUr017xed1irFRkOa12opqDkDc6ueZ63mK8dp1AnDeuztRTyTciH2pJLEqNnMxnIJQG6hijKb16Vaqj68CqSktLEdF9txaff6/P6336hyLc2mC958821Ox2dVPL+jx49ptwOSJDIBQbXExCG1sMpo1435bT8khRaKpylNI46mf1Z5KKXYxHER8kaRZYokSYqwRilxnJLlmk0cVZaMeZ4jhY3AIoqMIcVsNiNOEy5dulTFBbQ9l+OTx1i2YDweV+b66/X6qdb0UZQ4ManetVagrSI+XaGytD2sgjZc18FxHRzHxnY8bMvCdhykdLBdr6KT3/v2GyY+4mbDdDrnzbfe5mR8ys7eFXYLOmm1fJI4QuiCTrQu0mYUhj2qEHyEQOrKPQqtc5TUBkAbklG1H1bsU1V7VYXwLS1T0cTyM6lAdA5Zmhepoc2ekZSSPM2J0hiVa1SmSVNNGsckcVLRSZrDOtqgVE6W5WilsKQDwiaKI9arNdPpjCRNuLxz2aR2SVNs1+Xo9AjbEZydTphNpyzmS9ZFBuyLXMLib5M3h42bJeOumWrBjBtMucnUy2olMy4ZdRP+wvMDhefvU41RAWRzLs26dfNqHmFzWtX0mmAX1pVo3A/P9dwArvp6A3jqbw2AqwEwfKJuuQbqa0+A4tMV+5n19BQlSRLzRu8ak2spjYOusWQzeXSUULVnv+VUfk2WdMiF5PHxCQBXRpeYTOf85Fe/CsAffOv3yKIZSmWMx1MA/LbPdDrFtW1UYdGH0lgaSo1NuSlfpow/r1w1jEn9hTzwzzsJl6WMWl1KWaWJvAErXcXysy2B73hI4VQOyZ4tiPIU3/fJVF6FcEqShCiKSDYRy9WcIAhwHIf5fM5sNmO1WrBcr0nznHURfHe9XnN8fMzf/Yv9XH9tpaQT7ZtnKaXAsSykZRxyDWbk5FqilTYvLpZ58bCEQ6YFj49O0Fpz5dKIyXTGT371pwD4g2/9PnlBJ5PxFDT4nYDpbGZUfcJEj0BpZA52rskLNXAZyVxrkNpYRwiM9JxneRXdQmP2jUpQ08V+lajMHcrvFCpmI5UJZUzWVa6xLIGkoBPLDLpeR0gpsG1B5LhIAdEmAiHwLUGsEoTvk+Y5aZphWSZ/1OlmQxrFLFZzgsDHcWxm8zmz+ZTVasFqvSLJcjbLOfPFnM16w9Hx0YWnE1PCLW5fvvVXvLwJTE2GGjZBrHG9uNdsXX2t6jXAr5pC2Oiz7J9KkjHNwydAscaceh5VtXC7YjlG82ZIeO779pjbiNhcRlGnnGOjbVhDVNFP8/52vSee3VOUCyFJZakiz3TD/FtW/lKWdAoTcbuSsCyrVnOJQlXS6nRodTps0hRt2fz6b/wav/4bv8Y//ie/AsIiilMcz8XxXCO15bmJb5dm6FxVahqJZd6IlQZlQu58v8DP+gfjE7Ct4jsPVmWeojKSRFlHSsliuSyONWmSk6QpmyhmE8UsNxFZprAsByEsMqXJc00URUwmE04nY+bzOd1ul+l8xnK55OzshPv371fGJGWixTRNuX///l/2Z/vIS54pVKbJcmXUe8jCGs3GEjbSkkjbKbL0+lgNdSiAEoJWp02r2yFKM7R0+PXf+DX+h9/4Nf7xL/8KINlEKa7n4gYuaZ6hspxet0uaJKAVQgmT+gIbqUBobQ5VCDpSVC84UFj3WSbFiGXLIj6fKSafmABLgNA0DRJl0zpPgutJJGXAWVHUkcb3abVivlgwX65IEkWSJkSRiUe5jAyd2JaDtCwyZV724ihiMp1yMh6zmM/pdrpMZzNWywXj8SkPHtwnCFpkaYq0JL7vkuYJ9x88+Eh/8x+m1G/81AyYBhOmZNg8wUybzLgGmaJN+adk4o2x6nHCJxh2c151P+eAoAKQcLty4155fQscmmupl9dYSw2UlRRVAg3n7p97TvXawq11ls+l6mVr7cVcnyFI2c+sp6codeTuHKVKFVih/kIUviFUe1QCq0q7IYSg3emAbSz34mjDpb0Rf/qm+cf0D/6zv8f/9r/+L/zBH/6/BLYxn1stJwwGA5I4NkwHY7orhY2SGUJLcsMCyQtLrCYgNdV9P0iS+rC9qKa6r7S+U1pAEeMtkyboa5oay8EkSci1JkeQF0MrBVmagZakSU4cL4wjc5qxWMyMlWKWY9s2p6enJEnCcrlkuVxycHBAFEVGkixeDD6S6BpPWcxzUiiVo3JRSNaiSDBYprvAvHAUxgsGuCwkEr/TAscYisTRhqt7Q/70Ow8QAv7BP/x6QSffJrAd0BAvlwyGQ+IkRuai6BeE5aB0jlIWuTZ7fXkZsqmw3hNCorSq9iMtR1b3ZZl+Q2uE1IYGtajsREvn3ypBohRYhbWrVgJSI7mlKsOSgjTLAU2apORoMinIiqB8KtcmQ3DPIo0yok1kHJnjjNn/196Z/FiS3Pf9ExGZb696vU5Pz5BeRFAHQbRpgPJJvAi+WQZ0o0GJsKjLWM0RDUsA7WMedNTBlkYQIIg2dCEJzVgcGoLhv8A8eZNsuUlTpjy9zEx31/a2zIzVh4hcXnVrZqxpUa+B/HZnvZeZsWb9Kr4ZEb9lE+UE68nzjMcnTzC1ZrPdsFltefWVT1DWFaMsx3k6zxkvAor9AbydIfQG+P7SF+057SBbPFVYl6cZ4DtyaKmtI4qizdSV0xTfI8u9wb2Xdr9d/fSXy29zXCK0rk9N0qb3Re97U27XuI58eqy1R1b794qO2LiU5zngoEam0HgASHsx/WWwy0dDUlJKtlXJ+WrN+WpNNhlzcrbi9idf5fYnX+V/3H3Ib/3O7/JPfvHLnJyec3J6zsu3XqHWmotkANzO2pQik4pM5mQy1RHiQxIhalaFNK3yccz6UHwQibX3gkz75w15Rd9rzT5SVWvqyqBri9bxCAiMift4VVWxutiwXm3ZVSXORa8S49kUYwxlWUb1eu+YzmfMZrO4F7Xd8vjkhPV2y+IFUJyAtAybout656LnhxT6QiLj/tMoQ2UZSipkWk6TmWRbVZyv1pxdrFCjESenUU5e/uSr/M+7D3njd36XX/zyL3Fycs7J6QW3br1CpWvW6zXeJFdZaQ8skxIlMpRQXWReQisnXkTS8jLgpcC1MaZEaycVOxSDyruo59AZgIlEMs1sPmmDipCIImkCxlmRRhtLWWnqUqNLG2WlMnhEu3JQVnUrJ5u6xPlAPhoxnk3QxlDtKrQxGOuYLqbMZjMuzuMs/MmTJ2x2WxYvgOIENGNn0Z50ZNUjiERcT6dJ9xtSa/P1uapLW9DNLLrEPTLbI7yu/n6Ze0zSptpP2aVrG7GXPv7vCKPtQ3tedPf75TQtTz/2ar1UV9P3vXY3JRdFV1+/wx8TB0FSfUv9EBqvDy4td/geISU/e1kX2l1Kicoz5osF88UCbT0+wLbUbEvNJ//2K7z6iRv8s1/7Gq/ducNrd+7wf+/dJ8/HKJUTUgzwpo69/S4h01LfvqKEb39+PAUK2FecaBRE4n5cxmZXxWOz43y9YbXZsN6WrLclVW0QSiKzjCBEtNtxDoFiNBlzfBy9T9TGtW/zUkqOj48JAs5XFzjnWK1W7Ha7NiTFQSOtjnVyEmcicXD3qDwjBIGSxH0oFTX84pJqnFHN5nNm8wU6RbrdVYZtqflEkpOv/trXeO3OV/inSU5G+SQ6d80UQUVFBqmSnCQ3XgqZnN8mc4RA9EKRlvEErguY2NPei2kFUiUj3Mboly6iMCIa9zrj8clwN8sSCWcZUmWUumK7K2MQxHVcIt6UJZuypKoMQnZy4lyMNSaEYjSZcHwU5UQb3/AiUgiOjvblZL1es91Fp7OHjm6g7q5AN1voaKXHSS2psD/YPpV3r8SnKaXJe6n6vVlRe7GtvC2hX3JHF/32Fr0y+nUUbZG9Zj/jvNi/d7kZbXHFfhv7+VMb2pnnpXbs09zHx0EY8772G78dptMp8/mcyXjGdDplNlswHo8RQkUj3NAs+TW+/WSrQm1kxiSLS4EjmTHO4KXjOQDTPAOz5fryiOQwm5/63GfZbrfouuTl45vxog84byk3azIlGI9znjx5wmKxSIoJHtvoFasMQnT+Ke3TS3978aLSXlN/ia8f5VelcArt/gTdDC2EuACklGC5GHP92jEv3bgGQCYlV45nLJfLqHKOYzQaoUSMFbU4mnFxdsqVK1eQCs7Oztito7PdK1eXjMdjyk3J6ekpm82GPM/59X/1Bwe9lvPab/x2mEymLOZzRuMps8mU+eKI0XiMEpIsH4EXhPSclczToK4QCIzMmWSSEESSk8Ct5YIQYDpSCLPj+nKBzOI+5E997u+x2W7QdcXto5utKrh1lt1mjcoEk8mIk8dPWBwtEgE4tEvrw3ketfOkIPMe7yKJyTSzUlIis4DZ+BjiQwl8CvMuQvTW7m3cg1IqLgNKKfFJXlwTqBObDLQFy8WEa1ePuHXjGoEoJ1eP5xwfH5OPYsTm0WiEkgKlco6PZ6zOT1guryJU4OzsjHK1YjqfsbyyZDIZU65LTk9O2e625FnOr//rw5aToihCO/gmwuooIP7slrX6/9oSUr6iPe9mCJdKKtL9Ng8dYTRtaNN0pfc/uVT7PmN0be0TWj9lQ4IdSXb1dS2lJdC9Pu3VVLQzxC5dv8qmtKIpbu9oH20itYLnY8ybPY9CPi5ipN14+GBx3uC9xTkZHc16RdT1ja+XzeDdGD/GBZNIUkF4EAqXIss5Ibl+4wgP3L/3HgBf/ze/z+uvv85qdc79996PkXqvHLPblgQB2hpWqxWf+tSP8ejRo1ZxQgQPMosaxB8xjPaziOvyEqC4/K0tuoka7NA6qhRXOnrSWMwmGONYr7eMRiNmswnewa7ekknJKNlRSSnx3lKWJV5En3/TFI7h3oP77UY8L8Begw8OcAThCcHigsF5SwgSF7KkWefxJnprCETHxd6BVAqBJwSJIOCxiKTxh4xycuN6kpN33gMJX/+3v8/rX3md1eqM+++/z2Qy5trymF1VghQ4azl5f8WPffpTPH70PtEtUkjRZiTIKCfeOHyaBnoTZ0NSxVmVdwI5VYTk2FgSkMQ9SiGi49nG60TjjLaLiSVSnKgsug/0jronJwE4mk3R1rHe7BiNc2bTCc4GtvWWXEkmE4VHtco75a5q5WQ2naJrzf2HD5KJg3wh5KRoyKIljR5h9Abs7rQ3WDcDd9EMtkVHTAX7edpMzaDeVNZ93auvRwodLe7TU1dBL2XRpe4xY9vePhX1+9hvbvtMGhJ6qsb+/WKPpNpHVXRE1jzfrknF0+T2nHAQJNUsnUViiod1GuUbrTdPEyFOCBEVHHy3N2WlJUvKDFJkGA827QEYGVhtBRdnZ7zySnQzdHR0xJvf/ve88cZv8tY3vsHt27f5sx98nywIri6PKTdrbrx0k8ePH/cUH5oorrG90SHshxNVfwa1F6ivVUF+dhlBRGUAgOAD2jiqZPsCcDxf4JGRtJRkmY/Jc8Vma7HEDfYmBlW9q9lutyglUKMMFwIX6zXGmBR2frKnXXiwCHGv0lmLE6qVE2NBCk+oGs/iCmfByxznHSpTqOCx0qLI4+9EKrQTWA/4gJVwsQ1cnJ/xyqu3AFgsjviDt7/DG7/1m7z1zW/yysu3+cEPvkeOiC81mzU3br3E40eP4izJB4QIBCcQmUCogLcyGv66ENXkVWNb1ZglCGgdz6agiT6+nIVAGwZEiBjMUDTLgBDDHorklYK4V2eMQxtLpaOcLBdHeC+otEUowWg0IRspNtt1sq+TqCwDJai2FdvthiyTZKMcD1ysVxhtmM2TnLgXQE4KWmLqTxWK9h/7A3Hzvbg0ePfJKRVW7F3rVdix4D6pXCLFrk668toqmjZ0dbQUVjT19Kvea0Til6Kb2bStu1R3c63pS+8qPbJqq+z3mUvPrc1WXHqmPDccxJ4UIikl4PBB45zBOY21Buc1Vb2jqnbU9Za6Lqn1llqX7WHqCqNrjK6xKaRFbS21tVTG8fhsw7WXrlIaKA1kszkyn/DLr/9z3vj676GBmy/f5sqtG7x/8oTF8hjrPcbZromJbKRIg0tq84d2rWek+6zj2YiEnGUxTISQUQuv2RwvK40XEmtdaysWlwhlq5avbVQxN8awS4oTxhg2Pduo4+Njlsvli0NSwiPzANLhiXLiXY2zFmsrar2lqso9OdG2RtclVb1LcqLRVYU1BuMs2llqZym15fH5lms3o5xUVpDPZ6h8yi+//qu88XtJTm6/wpVbN3nv8RPmR8cY57HOtvs5cY8pek+Jvvx82pcSrYfXaGQB0cOET4QmWm0+paLHepEIal+jLpXhgaQWkmU5WZ4jVIazjtpYqspQlgYnRPI2IlBqlLRVJVLG2ZN1BiUlRht21b6crFYrtruS5fExy+Ml4/GkXWI8fDSDbXvWzhaK/o0mddG/1+XYH7gbIin2CanopW2zd+f9LO1nr85+m9sM3Vm/9e31frsv8WCvrO7aHtkVPeLqkVVMW1wim8tEdKkBTT965fea9FxwECQlpAfh0ma4xfka5w3OGayNZGVshbE1xjRH2X639Q5nNc5qbCI3beJRW8PV6wtqB+ebmvNNzXqn+cGf32N6dMzf/PSn+Oa/e5O/+/c/x8nFOTdu3+LJ2SllXbG8djUGnRNNOxvPEL2Q5R/Wtw9Y7tsnqQ9eRrE+ukvabHdstjvKKqqUex/wPrBebbnYrNtIw+v1mhAC2+2WsixpPExtNhu2222roBFCQGuN1ocfcVUID1iCd4jc4XwVX2isxQeDsQbjSoyrMVajTYUxO4xLspTkJASDdRpnDdoatLVoZ7hydY72sNpqzjcVq63mf//wHtOjI/7Wpz/FN5KcPDk/4/rtWzw5P6PUFcvrV2OIjcZ/rEqq8YAUIZlYhHbSLIjOZRHpty4D2TSLHihCXCIMPrQGwckpRfxrDcnfH7Sh4oVKtnwhWjGUpWGz28XYUZVhs90QiGYe2/WW1XoVPZU4z2a9JgTY7naUuxKZRTuvzTbKSZ5lyV9mwBgd7cUOHUX7oyOk5qMddIuUtj/A9gZn+gN570rBU2TXkFCTo0m/V1dXSNec3uV90uh3o+hxX49Mx2f0PAAADaxJREFUOtbcI8+2612Xu3YVXbZ+Q/szpbYfbf96BHepvj4pdn1+/iSVPceyPgaiNp8PDufBOoExdSQDoaKTTki2UxYZFD5InDdIkSHx+OTTziMIImtVfo3waAebVcW1azE+07133uUnP/vj3L17n9HEUtYVv/ovv8ZyecQffutbzI/mCGs5OzsjS3tbkZwAIZL91EdDf2lvL94QnUeL0AxOjS/AxFc6KWUEFz1sV0Yj1msAzs/POZrkrVskYzS5iRvjMvi4h6U11tStN4q+lt9onLHb1tjgEZlidrT4y/7yfmQQ0uOMI4wCemvIx0Q5UUAtk9GuwDqPwBKkJHiHUya6vBIBn8dBXvo5YZRhTXzGOniMF2wvKq5eHSOAd955l8989se5+737jMYmysm/+BrL4wV/+K1vsVjMkC7JiZJ42/j0azTIA06KtN8q2tAcgcZbBNH4VyfPFCmvc7Tu/aSI8aakTflEVPxoy/CBqvLJWW00cNZWs16t8AHOL85ZjDKc9VjrqGvNeDKG4BDBU2nDxDRy4sjyDIHAO4+cSMaTnM1FFf1bZorZ0fxH/Wv//0ax973Yu9EffOHS7KNoBt+iN4Dvl9P8bMopuqztCN3NSpoymvKKroQewTXXm/KKXhnt/X65/XqLS33sM2BLml29FP1y++kvP5tUZ1PvXp+aPvT71l3fY8DngIOYScW9KIe1Os2ODMbqtPRQxxlUmkVFH3YVVVVG/3z1jroqk1+7Gq0rtK7a87o23Htwys1bE04vNKcXmlc/eZv/9ifvoLIR5+sV9959yP2H7/KFL/48d776K0ymU6z3CCn3bKHaWVS0GP3I/euTUv/7h6GZFTkCQkmCF1SVpqo0q/UmBW50VFWF99HGrKoqtNZ7xsBNWUIIxtNJG724ccOU5zlHR0cfuT9/XfDeRh90psY4jak1xsXlqRiRtsLY9PvXNVVdUemSqq6oTRnlpIo2Z7Wp0LpMs8gabSz3Hpy0cnJyoXn1b9zmvzZysllx790HPHj3Xb7w87/AV776K4x7cuIISWO8CZuYXCa5qHnXklc6+vGZRFKrJzQzqMYwmGQz2Bn2NjZhwbc+ZwkhahU6EQ2FvYOqMmhtWK3XZHmG1o6qKiEEnLGUVfw7U1KidbOsHV0/CQST2ZRRPsJZRz7KIETD8xdBTqA/bhbsjcpFb4bQDsBdhv4yV/dRtDOHdmbWr6e4PE7vk0mXer/u9mYzO+mTWL/sXpOezt99duTW9qp/u9fJ5lrRq6Do1dW1rSvvMoE1WS91vuinez7Inmtpf0nEP0SL94IQFEJorI3ukOK8xca3R9/MSJow81GRIiOQqehxIhKBIBslv3cucOX4mLvff8J8Hm08/uyHT5hMJlTaIEYZk+mc9fkZf356wk985ie5c+cOf/T2t/nP3/0uV5bLdvB5mlYahY6/GB82k/og9MN85HmG1R5nI/HUdQ1CpJDxguPjY7wXbLdbRkpy7foVvPeMxhlSy5a4RqNRcrm0Q8nRjzwS8ceBJ4bJ8C5FblYS6yzSiCgn3tFEOg4+yolUAiUVJL96WZpt+Wmc3WS6BgLKBK5eWXL3e4+ZL2aEAP/nhydMJ2PKWiNGOeMkJ+enJ/zE3/kMd+58hT96+9v8l+/+J5bLKymcVJwWB9F5TXfOxXAd6ZrMBM4GlCQFOIykFpJGn/fRbsqHhnSiwXIQXawpIJJaawAcj9F4hC4d1sa4YXVVg5SU1Q5jopyE4NluNowyydVry+QRIxqz13WNkJI8j/aCm4sVUo7wLr3wfIiHlUNB0X4W3VkzA+kR1j67FB1h9K7vz5iKbmmtIa42XUd8zf1+eW1b2rL3iWK/jqdJq5lN7eVrUzWN7reJXpuaNE0TunIuz566Crt+tXVe6mPXz6aNqf1N5c8BB0FSclTiRIXwWTKwHWOsQ4RR67cvhIBPntIbx66ZiAO5GWWsq7gMNglzXJgjy0hS49GUbblFjQS1jR4mnNxwui2xTpMpy3vvPyTPM8yo5P2zR9z89FX+wZd/hrP5I/7XH/8pprRcW1zB155pfoTeWY4mCzabHVkXTYQm/Hvo+VCSUrZvvz40NlMxkxQSL5M6PT6OTnRv26pvb1ValMhaMt6tHe88XHPz5nVUEFybXOX07DHOCqZHC2Q+Zb1ZMfVjNptI+tPxVaqd4KLekecLsJaj+REnJyecm9Xz/JX+lUCoipBpTFAgFEFMMdYifI4UimycE6yLchJC9LUnBEEmV0RZxqpcI6RkKuf4MEOVNcEHxuMpm1ZO1gSinJxsS5zXKGV4/3GUE52XvHea5OSXfoazxSPu/vc/RZeWa/MrBB3lxJSOxXjOdrtDKcjyGAI+4MjGAq8dyKQubwOeEI14AzFsi4iunjKZEZQjuICXnuCiXRUOVIBMyTbUvFlrlMrJVHT/tF157j1Yc+PGNVQW5eTk7ATrJLPlESqfRTmZjdmsNc6NmI2vUJeCVb0jz+YEazk+usLJyQln+uKvVQY+ChrySGe9G/QG4f3PIn3fG4iL/VlBkX4Ue3maex2x7A3qXaN6aXrltG261M52qO/u7ZFck6Yht35be+1rjzZ9r5K2/l77+01qn8nlwi61pde6Hj3yvHAQJOWDRQSFIy47tNpvIfrTa3z7BRc1AOOsJMTAcQowIdqlAErVSJlhTFTBbfz8qUwgRCSEqt6hdYV1muDjsmEgw3tDXdecn58znU75/Oc/z8/97D/irW++yd0/+T4jRizn1zl/8hhMYDqd481f/Ubys2ZjEJUgJpMRWS6pU2iPxrWU1jrtVcXows315ogPp/GX+GLMpkIw+FrhlEdKsEaDB5EFRFD4EPsR5cQmDThPkAIlJEE7lBJxcM8j0eXGRH96QlGWKhrNJjmp9Q5dV5hGTsoK73OcM+hac35xwWwy4fM//dP83D/8Wd761lvc/ePvMxYjjufXOH18DjYwHs8IzmC1QymJ940aOnEyLhrrpxgyXjRukZqZd+xFcrfhCUK2e1YhLSumKVnM4lNsKxGZa7NeM5mMUJmkqnScmSWjZG2SopEWWBfJ3XqHcj6F9oCR8ljtcNa3L1yHjGa47L/VF82/vdlAk6i7Dh1ntHm6gtuyGoJoMhRNvl553Yyrq69oKmjqaEmml6efrvvY612fRJ5xt+OPfj1Fv57u2fTZqSPnPt3s92XvYbQPpd8/nisOgqTifomHkLWqtSGkN2Bp8SFrw2k0dlNChGh57wTBWZRKdlJSIchRMsZH8t5HmyvV7SNV9RptKqw1SFlT1xXaBMCiTcWjRxccLWa8/PJLnD464Utf+hLnj895+83v8OjBCTdv3uR4seTBOw+YjsYf2LcmHEOfBPbspD5g9eQvzJPO1+t1tGnJJdsb17HWIghYG413rbXUdXxmIqkiN+TlnEPbulVTfxFIynoHeILPUAiwaWnPhRT80rYk5dOmjRABL8EHFd1HubgUqLIMQk4md3EpLQSsN2lsj0uBtW7kRCNEIyclIVhqU7Jr5eQWZ49P+NIvfImLx+e8/ebbPHpwyq1bL3E8P+bhvYcxArIUbdDDQPOSJZJmeqfNlyQmqqen5T0hI5d5L5AqtN5XoCcnPXKLYhe1/9abDSqT5CPJbnstLQXG/adyF+UETbv35a1DC41zFu8sptIopbDWvBCmCu0gW/RIpegG25ae9kbioiMFegNzc5uOhIreeUdUTZ6muKKXvmvMHgH02tKSB126dvbVtoCu7rZd+30omrb0iHOvHy3ZdO1uuk/zfIr+9e65dM+JNk17uymX7vrzwkGQVNTuSy+DEqzVcVCW3cAcQkh7DvG9EjqvD94LsvS3k6lRilRbtaVbK5MvtJi+qrcYE9XVBVEpo6p2qAyycYZ1JaenT5iMc27evM6De/eZZFO+8MUvsDuv+A/f+Y88fHifK1eX1Nv6I/eyMwx++tqz8GEe1htFCWofgxYKR57FMpsgklo7CHEQb2ZQjQPfertpY1A1ShSHjbiE5y3RLx5RgUCqgPeSEEbpZWZfTkizrqAkSkWZMlojydCySrImsFZGxZi05FbWG6yJ6upQYaymrLZkGeSTHLcrOTl9wmSUc/PGNR7cu880n/KFL/7jPTlZXl1S7zSNqVSgWfZtFCaSzVzaV2oIrDHwbQTGEyLxpLW9+HdBu2dK7yVGpLqUSsERq4paB7a7HQjPKIt7XsbaaCxe1wgCSiqc8wRMjLUWPPV2TZZl1HVNlr0IctInov6A3Vy7PEgXXa6WOIredbq07UmTg2fkKXqE1Z23DWqI5RJRdIS2X3OfBNo62o+ny9gjuaKpv3fe73+vhfv9Sld7RFrQy9v07ann2BHd8yKqg/DdN2DAgAEDBjwLB6GCPmDAgAEDBjwLA0kNGDBgwICDxUBSAwYMGDDgYDGQ1IABAwYMOFgMJDVgwIABAw4WA0kNGDBgwICDxUBSAwYMGDDgYDGQ1IABAwYMOFgMJDVgwIABAw4WA0kNGDBgwICDxUBSAwYMGDDgYDGQ1IABAwYMOFgMJDVgwIABAw4WA0kNGDBgwICDxUBSAwYMGDDgYDGQ1IABAwYMOFgMJDVgwIABAw4WA0kNGDBgwICDxUBSAwYMGDDgYDGQ1IABAwYMOFgMJDVgwIABAw4WA0kNGDBgwICDxUBSAwYMGDDgYPH/ACAGWBvl6vTUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#显示原始图片  抵抗样本 以及两张图之间的差异  其中灰色代表没有差异的像素点\n",
    "show_images_diff(orig,adversary.original_label,adv,adversary.adversarial_label)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "advbox",
   "language": "python",
   "name": "advbox"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
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